Inzamam Mashood Nasir

@hitecuni.edu.pk

Lecturer, Computer Science Department
HITEC University Taxila

Inzamam Mashood Nasir


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https://researchid.co/inzamamm

RESEARCH INTERESTS

My research interests include Machine Learning, Computer Vision and Medical and Agricultural Imaging, Blockchain and IoTs.
49

Scopus Publications

1028

Scholar Citations

21

Scholar h-index

24

Scholar i10-index

Scopus Publications

  • A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans
    Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Sahar Mansour, Noha Negm, Asma A. Alhashmi, Mohannad A. Alharbi, Eunchan Kim
    Scientific Reports, 2026
    Lung cancer is an extremely fatal kind of cancer, resulting in the deaths of almost 7.6 million individuals annually around the globe. Nevertheless, a timely diagnosis is a crucial necessity for enhancing the likelihood of human survival. Regarding tumor identification, CT scans are normally used to identify affected areas. Nevertheless, CT imaging face significant problems such as poor visibility of tumor locations and high false negative rates. The small dataset size of medical imaging makes it challenging to capture local lesion features by iterative training, considering all input features equally. This work integrates Convolutional Neural Network (CNN) and Improved Swin Transformer (C-Swin), a deep learning model that extracts and integrates fine-grained local and global features. C-Swin has Transformer encoder and a CNN module. The CNN module extracts local features, whereas the Transformer module captures global features. The Transformer encoder uses a hybrid shifted window attention method to focus on a spatial region of the CT image, reducing background semantic information and improving local feature capture accuracy. The proposed method is validated using the publicly accessible Kaggle dataset namely IQ-OTH/NCCD with three classes. the proposed C-Swin model achieved average accuracy of 96.26%, precision of 97.48%, recall of 96.39% and F1-score of 97.42%. The numerical findings unequivocally demonstrate that our proposed method surpasses various existing methods with an increase in accuracy ranging from 2.31% to 6.81%. The C-Swin model is capable of extracting detailed local lesion features, resulting in improved classification performance.
  • Explainable brain tumor segmentation via attention-guided hybrid CNN–Transformer–Mamba network
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius
    Knowledge Based Systems, 2026
  • Multimodal Deep Learning with Attention-Based Fusion for Skin Cancer Diagnosis
    Wiem Abdelbaki, Hend Alshaya, Inzamam Mashood Nasir, Sara Tehsin, Wided Bouchelligua
    Bioengineering, 2026
    The diagnosis of skin cancer remains a growing challenge because of its high variability as a result of the varying imaging conditions in clinical settings. This paper proposes a multimodal deep learning framework to address these challenges by combining the auxiliary clinical information with dermoscopic image features. This proposed architecture uses an attention-based feature encoder with a structured multimodal fusion approach to utilize the integrated feature representation across all channels. Evaluations of the proposed architecture were conducted across a range of benchmark datasets, including ISIC 2019, ISIC 2020, and HAM10000, using a unified experimental approach. This proposed model achieved accuracies of 90.5%, 88.7%, and 91.8% and AUCs of 95.8%, 94.6%, and 96.3%, respectively, on the selected datasets. For the baseline models, ResNet50 and EfficientNet-B4, our approach increased the AUC by 6.5% and the F1 score by 8.0%. Furthermore, across various datasets, the model achieved an AUC of 90.9%, proposing strong generalization. From the ablation analysis results, the attention and multimodal fusion mechanisms showed a 4.1% decrease in AUC when key components were removed, confirming their effectiveness. With 34.7 million parameters and an average of 19.3 Ms., the model has adequate intensity to deploy in a real clinical setting without affecting its performance. Additionally, the improvements to the model were statistically significant across all evaluation metrics (p = 0.01). The proposed multimodal framework demonstrates strong performance and robustness across multiple benchmark datasets.
  • Transformer-Based Deep Learning for Population-Scale Retinal Image Screening of Ophthalmic Disorders
    Wiem Abdelbaki, Wided Bouchelligua, Inzamam Mashood Nasir, Sara Tehsin, Hend Alshaya
    Bioengineering, 2026
    To perform screening of the retina on a population scale, an automated procedure is required that incorporates accurate, reproducible, interpretable, and computationally costeffective models. Existing approaches using convolutional or transformer architectures typically do not adequately represent both fine-grained pathology and large-scale retinal context simultaneously, which could adversely affect their reliability if used for large-scale applications in clinical practice. In this paper, we propose a hierarchical transformer-based screening framework for retinal fundus images that incorporates patch-based tokenization, global transformer encoding, and hierarchical aggregation of contextual information. We also developed a lightweight prediction head that supports screening for both single and multiple diseases. The framework has been evaluated using standard screening metrics, robustness, and cross-dataset generalization analyses on two eye retinopathy image databases: EyePACS and RFMiD. With regard to screening for a binary outcome of diabetic retinopathy, our method provided an accuracy of 89.4% and an area under the receiver operating characteristic (AUROC) curve of 93.6% on EyePACS and attained an accuracy of 95.2% and a macro-averaged F1 score of 82.7% on RFMiD. Our hierarchical transformer achieved improved robustness to degraded images and increased generalizability across datasets compared with all current state-of-the-art models. The proposed hierarchical transformer demonstrates strong potential for large-scale retinal screening and provides a promising foundation for future clinically validated deployment.
  • Explainable skin cancer diagnosis with parallel attention mechanism for segmentation and classification
    Adel Thaljaoui, Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Oumaima Saidani, Emad Fadhal, Taoufik Saidani
    Biomedical Signal Processing and Control, 2026
  • Transformer-Based Foundation Learning for Robust and Data-Efficient Skin Disease Imaging
    Inzamam Mashood Nasir, Hend Alshaya, Sara Tehsin, Wided Bouchelligua
    Diagnostics, 2026
    Background/Objectives: Accurate and reliable automated dermoscopic lesion classification remains challenging. This is due to pronounced dataset bias, limited expert-annotated data, and poor cross-dataset generalization of conventional supervised deep learning models. In clinical dermatology, these limitations restrict the deployment of data-driven diagnostic systems across diverse acquisition settings and patient populations. Methods: Motivated by these challenges, this study proposes a transformer-based, dermatology-specific foundation model. The model learns transferable visual representations from large collections of unlabeled dermoscopic images via self-supervised pretraining. It integrates large-scale dermatology-oriented self-supervised learning with a hierarchical vision transformer backbone. This enables effective capture of both fine-grained lesion textures and global morphological patterns. The evaluation is conducted across three publicly available dermoscopic datasets: ISIC 2018, HAM10000, and PH2. The study assesses in-dataset, cross-dataset, limited-label, ablation, and computational-efficiency settings. Results: The proposed approach achieves in-dataset classification accuracies of 94.87%, 97.32%, and 98.17% on ISIC 2018, HAM10000, and PH2, respectively. It outperforms strong transformer and hybrid baselines. Cross-dataset transfer experiments show consistent performance gains of 3.5–5.8% over supervised counterparts. This indicates improved robustness to domain shift. Furthermore, when fine-tuned with only 10% of the labeled training data, the model achieves performance comparable to fully supervised baselines. Conclusions: This highlights strong data efficiency. These results demonstrate that dermatology-specific foundation learning offers a principled and practical solution for robust dermoscopic lesion classification under realistic clinical constraints.
  • CausaOne-sign: Causal explainable one-shot signature verification with lightweight cross-modality fusion
    Sara Tehsin, Inzamam Mashood Nasir, Ali Hassan, Farhan Riaz
    Ain Shams Engineering Journal, 2026
    Background: Offline handwritten signature verification remains a difficult biometrics problem due to large intra-writer variability; skilled forgers; the limited number of reference samples available; and the black-box nature of many current deep learning based decision-making methodologies. Objective: To develop an interpretable, efficient one-shot learning framework that can perform offline signature verification for individuals who have never been seen before using as few reference signatures as possible. Materials and Methods: The proposed CausaOne-Sign model uses stroke aware graph encoding, transformer based reasoning, and prototypical embeddings, along with a causal attribution model to provide an explanation of how signature verification works. Experiments have been conducted using CEDAR, SigComp2011 UTSig, and BHSig260 datasets. Results: CausaOne-Sign achieved up to 97.4% accuracy and 99.1% area under the curve (AUC), with low ERR (1.8%), outperforming or matching state-of-the-art methods. Conclusion CausaOne-Sign offers a robust, interpretable, and resource-efficient solution for OSV, suitable for forensic and mobile applications.
  • Cross-view remote sensing and street-level data fusion for intelligent traffic congestion analysis
    Inzamam Mashood Nasir, , Hend Alshaya, Sara Tehsin, Wided Bouchelligua, , and
    Aims Mathematics, 2026
    The issue of urban traffic congestion is a persistent problem for the sustainable management of cities through transportation systems, as there is a need for models that integrate and analyze heterogeneous sources to yield accurate, interpretable outcomes. This paper introduces the cross-view fusion network (CVF-Net), a new multimodal deep learning framework for analyzing congestion across entire cities by combining remote-sensing imagery (drone aerial views), street-view camera images, and graph-structured sensor data into a single model. This model is introduced through a very novel architecture that includes a hierarchical attention fusion transformer (HAFT), which fuses cross-view attention (CVA) between the aerial and ground view, a temporal graph neural network (TGNN) that uses a spatio-temporal dynamic, and a graph refinement (GR) network for consistency relative to the graph topology. Extensive experiments across three benchmarks (CityFlowV2, METR-LA, PEMS-BAY) demonstrate that CVF-Net consistently outperforms other recent state-of-the-art methods, reducing forecasting error (MAE) by 9.3% and increasing tracking continuity (IDF1) by 7.0%. Ablation studies suggest that hierarchical fusion and temporal modeling improve accuracy and stability, while sensitivity analyses show that attention maps capture congestion and causal temporal patterns, which are real symptoms of congestion. The model also shows strong cross-dataset generalizability and robustness to sensor noise, which extends its performance in the real world. Unlike existing spatio-temporal GNNs and multimodal Transformers that rely on flat feature aggregation or implicitly assume cross-view alignment, the proposed framework introduces a hierarchical, alignment-aware fusion strategy that explicitly integrates aerial visual context with graph-temporal traffic dynamics.
  • Mamba-RSI: a state-space deep learning framework for efficient land-use and land-cover classification in remote sensing imagery
    Wiem Abdelbaki, , Wided Bouchelligua, Inzamam Mashood Nasir, Sara Tehsin, Hend Alshaya, , , and
    Aims Mathematics, 2026
    Accurate and efficient land-use and land-cover (LULC) classification from remote sensing imagery remains challenging. This is because it requires capturing long-range spatial dependencies while maintaining computational scalability. Recent transformer-based models improve global context modeling. However, they suffer from quadratic complexity and are limited in applicability to high-resolution imagery. We introduce Mamba-RSI: a linear-time, state-space deep learning framework using selective recursion, hierarchical multi-scale feature extraction, and lightweight global representations. Mamba-RSI captures both fine-grained spectral/texture information and coarse structural patterns with significantly less computational overhead than existing quadratic self-attention transformers. Extensive experimentation on EuroSAT and NWPU-RESISC45 demonstrated that Mamba-RSI achieves state-of-the-art performance. It achieved 99.72% accuracy on EuroSAT and 96.84% on RESISC45. This represents a +0.40% improvement over the strongest transformer baseline, ATMformer, on EuroSAT, a +0.29% improvement on RESISC45, and more than +0.53% over ViT-B on EuroSAT. Robustness tests under severe Gaussian noise ($ \sigma = 0.10 $) showed that Mamba-RSI maintains 97.43% accuracy. MaxViT, by comparison, maintains 94.01% in the same setting. Mamba-RSI also preserves 91.15% accuracy under 30% patch occlusion, outperforming ViT-B by +7.41%. Mamba-RSI provides an attractive blend of accuracy, robustness, and efficiency. It serves as a scalable foundation for new insights into remote sensing analytics and LULC mapping systems.
  • Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors
    Wiem Abdelbaki, Hend Alshaya, Inzamam Mashood Nasir, Sara Tehsin, Salwa Said, Wided Bouchelligua
    Biomedicines, 2026
    Background: Diagnosing CNS tumors through MRI is limited by significant variability in scanner hardware, acquisition protocols, and intensity characteristics at clinical centers, resulting in substantial domain shifts that lead to diminished reliability for automated models. Methods: We present a Domain-Adaptive MRI Learning Model (DA-MLM) consisting of an adversarially aligned hybrid 3D CNN–transformer encoder with contrastive regularization and covariance-based feature harmonization. Varying sequence MRI inputs (T1, T1ce, T2, and FLAIR) were inputted to multi-scale convolutional layers followed by global self-attention to effectively capture localized tumor structure and long-range spatial context, with domain adaptation that harmonizes feature distribution across datasets. Results: On the BraTS 2020 dataset, we found DA-MLM achieved 94.8% accuracy, 93.6% macro-F1, and 96.2% AUC, improving upon previously established benchmarks by 2–4%. DA-MLM also attained Dice score segmentation of 93.1% (WT), 91.4% (TC), and 89.5% (ET), improving upon 2–3.5% for CNN and transformer methods. On the REMBRANDT dataset, DA-MLM achieved 92.3% accuracy with segmentation improvements of 3–7% over existing U-Net and expert annotations. Robustness testing indicated 40–60% less degradation under noise, contrast shift, and motion artifacts, and synthetic shifts in scanner location showed negligible performance impairment (<0.06). Cross-domain evaluation also demonstrated 5–11% less degradation than existing methods. Conclusions: In summary, DA-MLM demonstrates improved accuracy, segmentation fidelity, and robustness to perturbations, as well as strong cross-domain generalization indicating the suitability for deployment in multicenter MRI applications where variation in imaging performance is unavoidable.
  • ECSA-Net: A Lightweight Attention-Based Deep Learning Model for Eye Disease Detection
    Sara Tehsin, Muhammad John Abbas, Inzamam Mashood Nasir, Fadwa Alrowais, Reham Abualhamayel, Abdulsamad Ebrahim Yahya, Radwa Marzouk
    Computers Materials and Continua, 2026
  • Q-ALIGNer: A Quantum Entanglement-Driven Multimodal Framework for Robust Fake News Detection
    Sara Tehsin
    Computers Materials and Continua, 2026
  • HMA-DER: A Hierarchical Attention and Expert Routing Framework for Accurate Gastrointestinal Disease Diagnosis
    Sara Tehsin, Inzamam Mashood Nasir, Wiem Abdelbaki, Fadwa Alrowais, Khalid A. Alattas, Sultan Almutairi, Radwa Marzouk
    Computers Materials and Continua, 2026
  • A systematic literature review on advances in brain tumor detection using deep learning and explainable AI methods
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2025
  • Adaptive Vision–Language Transformer for Multimodal CNS Tumor Diagnosis
    Inzamam Mashood Nasir, Hend Alshaya, Sara Tehsin, Wided Bouchelligua
    Biomedicines, 2025
  • Hybrid State–Space and Vision Transformer Framework for Fetal Ultrasound Plane Classification in Prenatal Diagnostics
    Sara Tehsin, Hend Alshaya, Wided Bouchelligua, Inzamam Mashood Nasir
    Diagnostics, 2025
  • Hierarchical Multi-Stage Attention and Dynamic Expert Routing for Explainable Gastrointestinal Disease Diagnosis
    Muhammad John Abbas, Hend Alshaya, Wided Bouchelligua, Nehal Hassan, Inzamam Mashood Nasir
    Diagnostics, 2025
  • A multi-scale simplicial transformer with graph attention for facial emotion recognition
    Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Oumaima Saidani, Refka Ghodhbani, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani
    Ain Shams Engineering Journal, 2025
  • DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection
    Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir
    Results in Engineering, 2025
  • GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius
    Algorithms, 2025
  • FLTrans-Net: Transformer-based feature learning network for wheat head detection
    Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Sara Tehsin, Norma Latif Fitriyani, Muhammad Syafrudin
    Computers and Electronics in Agriculture, 2025
  • Interpreting CNN for Brain Tumor Classification Using XGrad-Cam
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius
    Communications in Computer and Information Science, 2025
  • Explainability of Disease Classification from Medical Images Using XGrad-Cam
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius
    Communications in Computer and Information Science, 2025
  • Explainable Cubic Attention-Based Autoencoder for Skin Cancer Classification
    Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Adam Zielonka, Marcin Woźniak
    Lecture Notes in Computer Science, 2025
  • Heuristic-Inspired Percentage Attribution Index: A Novel Measure for Explainability in Skin Cancer Classification
    Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Nazar Hussain, Muhammad Abdullah Bilal, Rytis Maskeliūnas
    Proceedings of the 2025 International Conference on Computational Intelligence Security and Artificial Intelligence Intellisecai 2025, 2025
  • NeuroMamba: Efficient State-Space Model for Explainable Brain Tumor Segmentation
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius, Nazar Hussain, Muhammad Abdullah Bilal, Rytis Maskeliūnas
    Proceedings of the 2025 International Conference on Computational Intelligence Security and Artificial Intelligence Intellisecai 2025, 2025
  • A Fusion Framework of Transformer and CNN for Non-small Cell Lung Cancer Classification
    Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Sara Tehsin, Muhammad Attique Khan, Jawad Ahmad, Wadii Boulila
    Lecture Notes in Networks and Systems, 2025
  • Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification
    Samia Nawaz Yousafzai, Fahd N. Al-Wesabi, Hadeel Alsolai, Shouki A. Ebad, Inzamam Mashood Nasir, Emad Fadhal, Adel Thaljaoui
    Peerj Computer Science, 2025
  • Efficient hybrid CNN-transformer model for accurate blood cancer detection
    Wiem Abdelbaki, Muhammad John Abbas, Fathimathul Rajeena P. P, Inzamam Mashood Nasir, Deema Mohammed Alsekait, Adel Thaljaoui, Diaa Salama AbdElminaam
    Peerj Computer Science, 2025
  • MFAN: Multi-Feature Attention Network for Breast Cancer Classification
    Inzamam Mashood Nasir, Masad A. Alrasheedi, Nasser Aedh Alreshidi
    Mathematics, 2024
  • Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection
    Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas
    Algorithms, 2024
  • X-News dataset for online news categorization
    Samia Nawaz Yousafzai, Hooria Shahbaz, Armughan Ali, Amreen Qamar, Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius
    International Journal of Intelligent Computing and Cybernetics, 2024
  • DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius, Rytis Maskeliūnas
    Big Data and Cognitive Computing, 2024
  • Enhancing Signature Verification Using Triplet Siamese Similarity Networks in Digital Documents
    Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir, Norma Latif Fitriyani, Muhammad Syafrudin
    Mathematics, 2024
  • Block Cipher Nonlinear Component Generation via Hybrid Pseudo-Random Binary Sequence for Image Encryption
    Dania Saleem Malik, Tariq Shah, Sara Tehsin, Inzamam Mashood Nasir, Norma Latif Fitriyani, Muhammad Syafrudin
    Mathematics, 2024
  • PLG-SUNetT: Parallel Local-Global Swin-UNet Transformer for Brain Tumor Detection
    Sara Tehsin, Inzamam Mashood Nasir, Robertas Damasevicius, Rytis Maskeliunuas
    2024 5th International Conference on Innovative Computing Icic 2024 Proceedings, 2024
  • MRA-Net: Multiscale Residual Attention Network for Multiclass Alzheimer Disease Classification
    Samia Nawaz Yousafzai, Inzamam Mashood Nasir, Sara Tehsin, Junaid Ali Khan
    2024 5th International Conference on Innovative Computing Icic 2024 Proceedings, 2024
  • Integrating Non-local Information with Residual Blocks to Generate Explainable Attention Maps of Skin Cancer
    Inzamam Mashood Nasir, Sara Tehsin, Robertas Damasevicius, Rytis Maskeliunas
    2024 5th International Conference on Innovative Computing Icic 2024 Proceedings, 2024
  • ENGA: Elastic Net-Based Genetic Algorithm for human action recognition
    Inzamam Mashood Nasir, Mudassar Raza, Siti Maghfirotul Ulyah, Jamal Hussain Shah, Norma Latif Fitriyani, Muhammad Syafrudin
    Expert Systems with Applications, 2023
  • Improved Shark Smell Optimization Algorithm for Human Action Recognition
    Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Muhammad Attique Khan, Yun-Cheol Nam, Yunyoung Nam
    Computers Materials and Continua, 2023
  • HAREDNet: A deep learning based architecture for autonomous video surveillance by recognizing human actions
    Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Shui-Hua Wang, Usman Tariq, Muhammad Attique Khan
    Computers and Electrical Engineering, 2022
  • Human Action Recognition using Machine Learning in Uncontrolled Environment
    Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Muhammad Attique Khan, Amjad Rehman
    2021 1st International Conference on Artificial Intelligence and Data Analytics Caida 2021, 2021
  • Customer prioritization for medical supply chain during COVID-19 pandemic
    Iram Mushtaq, Muhammad Umer, Muhammad Imran, Inzamam Mashood Nasir, Ghulam Muhammad, Mohammad Shorfuzzaman
    Computers Materials and Continua, 2021
  • An optimized approach for breast cancer classification for histopathological images based on hybrid feature set
    Inzamam Mashood Nasir, Muhammad Rashid, Jamal Hussain Shah, Muhammad Sharif, Muhammad Yahiya Haider Awan, Monagi H. Alkinani
    Current Medical Imaging, 2021
  • A blockchain based framework for stomach abnormalities recognition
    Muhammad Attique Khan, Inzamam Mashood Nasir, Muhammad Sharif, Majed Alhaisoni, Seifedine Kadry, Syed Ahmad Chan Bukhari, Yunyoung Nam
    Computers Materials and Continua, 2021
  • Pearson correlation-based feature selection for document classification using balanced training
    Inzamam Mashood Nasir, Muhammad Attique Khan, Mussarat Yasmin, Jamal Hussain Shah, Marcin Gabryel, Rafał Scherer, Robertas Damaševičius
    Sensors Switzerland, 2020
  • SCNN: A secure convolutional neural network using blockchain
    Inzamam Mashood Nasir, Muhammad Attique Khan, Ammar Armghan, Muhammad Younus Javed
    2020 2nd International Conference on Computer and Information Sciences Iccis 2020, 2020
  • A hybrid deep learning architecture for the classification of superhero fashion products: An application for medical-tech classification
    Inzamam Mashood Nasir, Muhammad Attique Khan, Majed Alhaisoni, Tanzila Saba, Amjad Rehman, Tassawar Iqbal
    CMES Computer Modeling in Engineering and Sciences, 2020
  • Deep learning-based classification of fruit diseases: An application for precision agriculture
    Inzamam Mashood Nasir, Asima Bibi, Jamal Hussain Shah, Muhammad Attique Khan, Muhammad Sharif, Khalid Iqbal, Yunyoung Nam, Seifedine Kadry
    Computers Materials and Continua, 2020

RECENT SCHOLAR PUBLICATIONS

  • Transformer-Driven Explainable Deep Learning with Quantitative Attribution Validation for Liver Tumor Detection
    IM Nasir, H Alshaya, S Tehsin, W Bouchelligua
    Bioengineering , 2026
    2026
  • Multimodal Deep Learning with Attention-Based Fusion for Skin Cancer Diagnosis
    W Abdelbaki, H Alshaya, IM Nasir, S Tehsin, W Bouchelligua
    Bioengineering , 2026
    2026
  • Explainable brain tumor segmentation via attention-guided hybrid CNN–Transformer–Mamba network
    S Tehsin, IM Nasir, R Damaševičius
    Knowledge-Based Systems, 116000 , 2026
    2026
  • Transformer-Based Deep Learning for Population-Scale Retinal Image Screening of Ophthalmic Disorders
    W Abdelbaki, W Bouchelligua, IM Nasir, S Tehsin, H Alshaya
    Bioengineering 13 (4), 377 , 2026
    2026
  • A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans
    SN Yousafzai, IM Nasir, S Mansour, N Negm, AA Alhashmi, MA Alharbi, ...
    Scientific Reports , 2026
    2026
    Citations: 3
  • Explainable skin cancer diagnosis with parallel attention mechanism for segmentation and classification
    A Thaljaoui, SN Yousafzai, IM Nasir, O Saidani, E Fadhal, T Saidani
    Biomedical Signal Processing and Control 113, 109159 , 2026
    2026
    Citations: 3
  • Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer Lesions
    K Rana, MAA Khan, T Akram, IM Nasir, S Tehsin
    2026
  • Transformer-Based Foundation Learning for Robust and Data-Efficient Skin Disease Imaging
    IM Nasir, H Alshaya, S Tehsin, W Bouchelligua
    Diagnostics 16 (3), 440 , 2026
    2026
    Citations: 4
  • CausaOne-sign: Causal explainable one-shot signature verification with lightweight cross-modality fusion
    S Tehsin, IM Nasir, A Hassan, F Riaz
    Ain Shams Engineering Journal 17 (2), 104002 , 2026
    2026
  • Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors
    W Abdelbaki, H Alshaya, IM Nasir, S Tehsin, S Said, W Bouchelligua
    Biomedicines 14 (1), 235 , 2026
    2026
    Citations: 2
  • ECSA-Net: a lightweight attention-based deep learning model for eye disease detection
    S Tehsin, MJ Abbas, IM Nasir, F Alrowais, R Abualhamayel, AE Yahya, ...
    Computers, materials and continua. 87 (2), 1-34 , 2026
    2026
  • Q-ALIGNer: a quantum entanglement-driven multimodal framework for robust fake news detection
    S Tehsin, IM Nasir, W Abdelbaki, F Alrowais, R Abualhamayel, AE Yahya, ...
    Computers, materials and continua. 87 (22), 1-31 , 2026
    2026
  • Mamba-RSI: a state-space deep learning framework for efficient land-use and land-cover classification in remote sensing imagery
    W Abdelbaki, W Bouchelligua, IM Nasir, S Tehsin, H Alshaya
    AIMS Mathematics. 11 (3), 5600-5647 , 2026
    2026
    Citations: 1
  • HMA-DER: a hierarchical attention and expert routing framework for accurate gastrointestinal disease diagnosis
    S Tehsin, IM Nasir, W Abdelbaki, F Alrowais, KA Alattas, S Almutairi, ...
    Computers, materials and continua. 87 (1), 1-36 , 2026
    2026
  • Cross-view remote sensing and street-level data fusion for intelligent traffic congestion analysis
    IM Nasir, H Alshaya, S Tehsin, W Bouchelligua
    AIMS Mathematics 11 (1), 1547-1589 , 2026
    2026
  • Heuristic-Inspired Percentage Attribution Index: A Novel Measure for Explainability in Skin Cancer Classification
    IM Nasir, S Tehsin, R Damaševičius, N Hussain, MA Bilal, R Maskeliūnas
    2025 International Conference on Computational Intelligence, Security, and … , 2025
    2025
    Citations: 1
  • NeuroMamba: Efficient State-Space Model for Explainable Brain Tumor Segmentation
    S Tehsin, IM Nasir, R Damaševičius, N Hussain, MA Bilal, R Maskeliūnas
    2025 International Conference on Computational Intelligence, Security, and … , 2025
    2025
  • Efficient hybrid CNN-transformer model for accurate blood cancer detection
    W Abdelbaki, MJ Abbas, IM Nasir, DM Alsekait, A Thaljaoui, ...
    PeerJ Computer Science 11, e3335 , 2025
    2025
    Citations: 2
  • Adaptive Vision–Language Transformer for Multimodal CNS Tumor Diagnosis
    IM Nasir, H Alshaya, S Tehsin, W Bouchelligua
    Biomedicines 13 (12), 2864 , 2025
    2025
  • A systematic literature review on advances in brain tumor detection using deep learning and explainable AI methods
    S Tehsin, IM Nasir, R Damaševičius
    Network Modeling Analysis in Health Informatics and Bioinformatics 14 (1), 154 , 2025
    2025
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Pearson Correlation Based Feature Selection for Document Classification using Balanced Training
    IM Nasir, MA Khan, M Yasmin, JH Shah, M Gabryel, R Scherer, ...
    Sensors , 2020
    2020
    Citations: 183
  • Deep learning-based classification of fruit diseases: An application for precision agriculture
    IM Nasir, A Bibi, JH Shah, MA Khan, M Sharif, K Iqbal, Y Nam, S Kadry
    Computers, Materials, & Continua 66 (2), 1949 , 2021
    2021
    Citations: 139
  • HAREDNet: A deep learning based architecture for autonomous video surveillance by recognizing human actions
    IM Nasir, M Raza, JH Shah, SH Wang, U Tariq, MA Khan
    Computers and Electrical Engineering 99, 1-17 , 2022
    2022
    Citations: 67
  • Human action recognition using machine learning in uncontrolled environment
    IM Nasir, M Raza, JH Shah, MA Khan, A Rehman
    2021 1st International Conference on Artificial Intelligence and Data … , 2021
    2021
    Citations: 62
  • A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products: An Application for Medical-Tech Classification
    IM Nasir, MA Khan, M Alhaisoni, T Saba, A Rehman, T Iqbal
    Computer Modeling in Engineering and Sciences 124 (3), 1017-1033 , 2020
    2020
    Citations: 54
  • DaSAM: Disease and spatial attention module-based explainable model for brain tumor detection
    S Tehsin, IM Nasir, R Damaševičius, R Maskeliūnas
    Big Data and Cognitive Computing 8 (9), 97 , 2024
    2024
    Citations: 45
  • A Blockchain based Framework for Stomach Abnormalities Recognition
    MA Khan, IM Nasir*, M Sharif, M Alhaisoni, S Kadry, SAC Bukhari, Y Nam
    CMC-Computers, Materials & Continua, 1-15 , 2020
    2020
    Citations: 39
  • ENGA: Elastic net-based genetic algorithm for human action recognition
    IM Nasir, M Raza, SM Ulyah, JH Shah, NL Fitriyani, M Syafrudin
    Expert Systems with Applications 227, 120311 , 2023
    2023
    Citations: 37
  • An optimized approach for breast cancer classification for histopathological images based on hybrid feature set
    IM Nasir, M Rashid, JH Shah, M Sharif, MYH Awan, MH Alkinani
    Current Medical Imaging 17 (1), 136-147 , 2021
    2021
    Citations: 34
  • Improved shark smell optimization algorithm for human action recognition
    I Nasir, M Raza, J Shah, M Khan, YC Nam, Y Nam
    Computers, Materials, & Continua 76 (3), 2667 , 2023
    2023
    Citations: 32
  • SCNN: A Secure Convolutional Neural Network using Blockchain
    IM Nasir, MA Khan, A Armghan, MY Javed
    2020 International Conference on Computer and Information Sciences (ICCIS), 1-6 , 2020
    2020
    Citations: 31
  • Gatransformer: A graph attention network-based transformer model to generate explainable attentions for brain tumor detection
    S Tehsin, IM Nasir, R Damaševičius
    Algorithms 18 (2), 89 , 2025
    2025
    Citations: 26
  • Integrating explanations into CNNs by adopting spiking attention block for skin cancer detection
    IM Nasir, S Tehsin, R Damaševičius, R Maskeliūnas
    Algorithms 17 (12), 557 , 2024
    2024
    Citations: 25
  • MFAN: Multi-Feature Attention Network for Breast Cancer Classification
    IM Nasir, MA Alrasheedi, NA Alreshidi
    Mathematics 12 (23), 1-19 , 2024
    2024
    Citations: 25
  • Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic
    I Mushtaq, M Umer, M Imran, IM Nasir, G Muhammad, M Shorfuzzaman
    CMC-Computers, Materials & Continua 70 (1), 59-72 , 2021
    2021
    Citations: 25
  • Block cipher nonlinear component generation via hybrid pseudo-random binary sequence for image encryption
    DS Malik, T Shah, S Tehsin, IM Nasir, NL Fitriyani, M Syafrudin
    Mathematics 12 (15), 2302 , 2024
    2024
    Citations: 24
  • Enhancing Signature Verification Using Triplet Siamese Similarity Networks in Digital Documents
    S Tehsin, A Hassan, F Riaz, IM Nasir, NL Fitriyani, M Syafrudin
    Mathematics 12 (17), 27-57 , 2024
    2024
    Citations: 23
  • Fast intra mode selection in HEVC using statistical model
    J Tariq, A Alfalou, A Ijaz, A Hashim, I Ashraf, H Rahman, A Armghan, ...
    Computers, Materials, & Continua 70 (2), 3903 , 2022
    2022
    Citations: 23
  • FLTrans-Net: Transformer-based feature learning network for wheat head detection
    SN Yousafzai, IM Nasir, S Tehsin, NL Fitriyani, M Syafrudin
    Computers and Electronics in Agriculture 229, 109706 , 2025
    2025
    Citations: 22
  • X-News Dataset for Online News Categorization
    SN Yousafzai, H Shahbaz, A Ali, A Qamar, IM Nasir, S Tehsin, ...
    International Journal of Intelligent Computing and Cybernetics 1 (1), 1-53 , 2024
    2024
    Citations: 22