Dr. K. Rasool Reddy

@smec.ac.in

Associate Professor and Department of ECE
St. Martin's Engineering College

Dr. K. Rasool Reddy


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https://researchid.co/rasool0812

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Signal Processing
20

Scopus Publications

185

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Enhanced diabetes prediction using CTGAN-MLP approach on body composition data
    Javad Hassannataj Joloudari, Mohammad Maftoun, Mohammad Ali Nematollahi, Kandala N. V. P. S. Rajesh, S. Prasanth Vaidya, Kamireddy Rasool Reddy, Pirhossein Kolivand
    Scientific Reports, 2026
    Accurate diabetes risk prediction is essential for timely intervention and effective disease management. To address these issues, this study evaluates a prediction framework that incorporates Conditional Tabular Generative Adversarial Network (CTGAN) to generate additional synthetic samples and mitigate class imbalance. Unlike interpolation-based oversampling techniques, CTGAN models the underlying data distribution and may better preserve nonlinear relationships among body composition variables. When combined with a Multilayer Perceptron (MLP), this approach enables the model to capture complex feature interactions that could be relevant for distinguishing individuals with diabetes from healthy participants. In our experiments, the CTGAN-augmented MLP achieved an accuracy of 93.91%, an AUC of 93.87%, a precision of 94.48%, and an F1-score of 93.89% under stratified 5-fold cross-validation, representing the highest performance among the evaluated models. The SHapley Additive exPlanations (SHAP) analysis was further employed to enhance interpretability and provided insight into the contribution of key predictors such as fat percentage, fat-free mass, and basal metabolic rate.
  • Identification and Categorization of Diabetic Retinopathy Using Modified Rider Optimization Algorithm
    D. Nagadevi, Neetu Chikyal, Kamireddy Rasool Reddy
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2026
  • DBTNet: A Dual-Stream Neural Network for Effective Brain Tumor Detection in MRI Images
    Rasool Reddy Kamireddy, Vijayakumar Kadha, Kandala NVPS Rajesh, Ravindra Dhuli, Sadiq Hussain
    Arabian Journal for Science and Engineering, 2025
  • A Hybrid Framework for the Diagnosis of Parkinson’s Disease using Handwritten Drawings-Spiral and Wave
    Rasool Reddy K, N V P S Rajesh Kandala, Srinivasu Polinati, Ravindra Dhuli
    Journal of Scientific and Industrial Research, 2025
    Parkinson's disease is a progressive neurological disorder that significantly affects individuals worldwide. Early and accurate classification of the disease is crucial for timely intervention and improved patient outcomes. This study aims to develop an effective classification system using drawings of spirals and waves to discriminate between healthy individuals and those with Parkinson's disease, aiming to provide an early diagnostic method, leading to improved patient lifespan. The study utilizes two sets of drawings: spirals and waves. Data augmentation techniques are employed to increase the dataset size and enhance training data for deep neural networks. The Pyramid Histogram of Oriented Gradients (PHoG) algorithm is applied to compute shape descriptors from healthy and Parkinson's drawings. A Visual Geometry Group (VGG)-based deep learning model is used to extract significant features from the modified drawings, particularly from the fc6 and fc7 layers. Supervised classifiers, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are employed individually and in combination to classify the extracted features. The results demonstrate that the fused features achieved the highest accuracy values: 98.6% for spiral drawings using SVM and 96.57% for wave drawings using KNN. These accuracy rates highlight the effectiveness of the proposed method in accurately classifying Parkinson's disease based on drawings of spirals and waves. The findings suggest that the proposed method has the potential to serve as a non-invasive and reliable tool for early diagnosis of Parkinson's disease. It can enable timely interventions and improved patient care.
  • Optimizing Brain Tumor Classification: Integrating Deep Learning and Machine Learning with Hyperparameter Tuning
    Vijaya Kumar Velpula, Kamireddy Rasool Reddy, K. Naga Prakash, K. Prasanthi Jasmine, Vadlamudi Jyothi Sri
    Engineering Proceedings, 2025
    Brain tumors significantly impact global health and pose serious challenges for accurate diagnosis due to their diverse nature and complex characteristics. Effective diagnosis and classification are essential for selecting the best treatment strategies and forecasting patient outcomes. Currently, histopathological examination of biopsy samples is the standard method for brain tumor identification and classification. However, this method is invasive, time-consuming, and prone to human error. To address these limitations, a fully automated approach is proposed for brain tumor classification. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in improving the accuracy and efficiency of tumor detection from magnetic resonance imaging (MRI) scans. In response, a model was developed that integrates machine learning (ML) and deep learning (DL) techniques. The process began by splitting the data into training, testing, and validation sets. Images were then resized and cropped to enhance model quality and efficiency. Relevant texture features were extracted using a modified Visual Geometry Group (VGG) architecture. These features were fed into various supervised ML models, including support vector machine (SVM), k-nearest neighbors (KNN), logistic regression (LR), stochastic gradient descent (SGD), random forest (RF), and AdaBoost, with GridSearchCV used for hyperparameter tuning. The model’s performance was evaluated using key metrics such as accuracy, precision, recall, F1-score, and specificity. Experimental results demonstrate that the proposed approach offers a robust and automated solution for brain tumor classification, achieving the highest accuracy of 94.02% with VGG19 and 96.30% with VGG16. This model can significantly assist healthcare professionals in early tumor detection and in improving diagnostic accuracy.
  • Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding
    Rasool Reddy Kamireddy, Rajesh N. V. P. S. Kandala, Ravindra Dhuli, Srinivasu Polinati, Kamesh Sonti, Ryszard Tadeusiewicz, Paweł Pławiak
    Plos One, 2024
    Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.
  • MFeRNet: A Deep CNN Approach for Detecting Median Filter Tampering in Re-Compressed Images
    Vijayakumar Kadha, Kamireddy Rasool Reddy, Santos Kumar Das, Madhusudhan Mishra
    Proceedings of 2024 IEEE 29th Asia Pacific Conference on Communications Sustainable Connectivity Advancing Green Technologies for A Smarter Future Apcc 2024, 2024
    The utilisation of median filtering (MF), a nonlinear signal processing technique, offers distinct advantages within picture anti-forensics. Consequently, there has been an increased focus on the forensic investigation of MF. However, due to lossy compression, identifying MF in the compressed domain is challenging. Towards this, research presents a novel approach for forensic analysis of MF in compressed images based on utilising deep noise residuals. In this framework, median filtering residuals (MFR) are employed to preprocess the images by passing through two streams. After that, the MFR output is extended to encompass two parallel blocks with different dilation rates to form a fusion feature vector. Further, the MFeRNet framework incorporates convolution, specifically developed to enhance information integration from several streams compared to conventional techniques. The proposed method, MFeRNet, aims to effectively integrate the three-level information of an image and comprehensively extract forensic clues in a compressed scenario. In addition, the experimental results demonstrate that the proposed methodology exhibits superior performance and reduced training time compared to the early reported techniques with equivalent convolution depth.
  • BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images
    K. Rasool Reddy, Kandala N. V. P. S. Rajesh, Ravindra Dhuli, Vuddagiri Ravi Kumar
    Frontiers in Human Neuroscience, 2024
    IntroductionBrain cancer is a frequently occurring disease around the globe and mostly developed due to the presence of tumors in/around the brain. Generally, the prevalence and incidence of brain cancer are much lower than that of other cancer types (breast, skin, lung, etc.). However, brain cancers are associated with high mortality rates, especially in adults, due to the false identification of tumor types, and delay in the diagnosis. Therefore, the minimization of false detection of brain tumor types and early diagnosis plays a crucial role in the improvement of patient survival rate. To achieve this, many researchers have recently developed deep learning (DL)-based approaches since they showed a remarkable performance, particularly in the classification task.MethodsThis article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using ‘He Normal’ initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments.Results and discussionEmpirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients.
  • Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor
    K. Rasool Reddy, Ravindra Dhuli
    Journal of Supercomputing, 2023
  • A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors
    K. Sowjanya, K.R. Reddy, M. Raveena
    International Journal of Computing and Digital Systems, 2023
    Brain tumors can develop at any brain location with uneven boundaries and shapes.Typically, they increased rapidly due to their size doubling in twenty-five days.If they were unrecognized in earlier phases, patients suffered from various medical problems, including death.Therefore, the identification of brain tumors in the earlier stages is one of the critical aspects.In addition, an effective imaging sequence also plays a vital role in tumor diagnosis.Magnetic resonance (MR) imaging is widely used among the available scanning approaches.Therefore, in this article, we develop a distinctive novel method to classify MR-based brain images.Here, initially, we improve the brightness of brain MR images using a median filter, and then we employ image data augmentation to increase the model's accuracy.Later, we obtain the region of interest (ROI) by Otsu's thresholding and morphological operations.Then, we extracted relevant local textures and shaped informative features from the ROI using Enhanced gradient local binary patterns (EGLBPs) and Modified pyramid histogram of oriented gradients (MPHOG).Finally, we perform classification using various supervised learning approaches: support vector machine (SVM), K-nearest neighbors (KNN), and ensemble learning.All these experiments are implemented on Harvard Medical School (HMS) database.Based on the simulation results, our proposed imaging system outperformed state-of-the-art methods in classification and segmentation.Hence, our suggested framework can be used as a predictive tool for diagnosing patients with brain tumors.
  • A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images
    Kamireddy Rasool Reddy, Ravindra Dhuli
    Diagnostics, 2023
  • A Machine Learning-Based Approach for the Prediction of Cardiovascular Diseases †
    Rasool Reddy Kamireddy, Nagadevi Darapureddy
    Engineering Proceedings, 2023
  • Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features
    K. Rasool Reddy, Raj Kumar Batchu, Srinivasu Polinati, Durga Prasad Bavirisetti
    Frontiers in Human Neuroscience, 2023
  • Image defogging algorithm based on fusion approach
    Journal of Advanced Research in Dynamical and Control Systems, 2019
  • GUI implementation of image encryption and decryption using Open CV-Python script on secured TFTP protocol
    K. Rasool Reddy, Ch. Madhava Rao
    Aip Conference Proceedings, 2018
  • Performance assessment of edge preserving filters
    Kamireddy Rasool Reddy, Madhava Rao Ch, Nagi Reddy Kalikiri
    International Journal of Information System Modeling and Design, 2017
  • Objective quality assessments of restoration images
    Rasool Reddy Kamireddy, Shivaramakrishna Punem, Supriya Jangala, Geetha Ramakrishna Dutt Chamarthi, Kota Yedukondalu Srinivas
    Advances in Intelligent Systems and Computing, 2017
  • Analysis of Non Stationary Data Signals Using Hilbert Haung Transform
    K. Rasool Reddy, K. Pranay Rayel, Ch. Madhava Rao
    Proceedings 2015 International Conference on Computational Intelligence and Communication Networks Cicn 2015, 2016
  • Steganography Based Secret Image Sharing Using Block Division Technique
    M. Tulasidasu, B. Lakshmi Sirisha, K. Rasool Reddy
    Proceedings 2015 International Conference on Computational Intelligence and Communication Networks Cicn 2015, 2016
  • Object Detection and Tracking - A Survey
    K. Rasool Reddy, K. Hari Priya, N. Neelima
    Proceedings 2015 International Conference on Computational Intelligence and Communication Networks Cicn 2015, 2016

RECENT SCHOLAR PUBLICATIONS

  • EEG-Based Seizure Detection Using Temporal Convolutional Networks and Tree-Based Models
    KR Reddy, V Kadha, JN Surekha, PA Kumar, M Mishra
    2026 22nd IEEE International Colloquium on Signal Processing & Its … , 2026
    2026
  • Enhanced diabetes prediction using CTGAN-MLP approach on body composition data
    JH Joloudari, M Maftoun, MA Nematollahi, KN Rajesh, SP Vaidya, ...
    Scientific Reports , 2025
    2025
    Citations: 1
  • DBTNet: A Dual-Stream Neural Network for Effective Brain Tumor Detection in MRI Images
    RR Kamireddy, V Kadha, KN Rajesh, R Dhuli, S Hussain
    Arabian Journal for Science and Engineering 50 (23), 19789-19803 , 2025
    2025
    Citations: 3
  • Optimizing Brain Tumor Classification: Integrating Deep Learning and Machine Learning with Hyperparameter Tuning
    VK Velpula, KR Reddy, KN Prakash, KP Jasmine, V Jyothi Sri
    Engineering Proceedings 87 (1), 64 , 2025
    2025
    Citations: 2
  • A Hybrid Framework for the Diagnosis of Parkinson’s Disease using Handwritten Drawings-Spiral and Wave
    KR Reddy, KN Rajesh, S Polinati, R Dhuli
    Journal of Scientific & Industrial Research 84, 520-530 , 2025
    2025
    Citations: 1
  • Mfernet: a deep cnn approach for detecting median filter tampering in re-compressed images
    V Kadha, KR Reddy, SK Das, M Mishra
    2024 IEEE 29th Asia Pacific Conference on Communications (APCC), 246-251 , 2024
    2024
    Citations: 1
  • Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding
    RR Kamireddy, RN Kandala, R Dhuli, S Polinati, K Sonti, R Tadeusiewicz, ...
    PloS one 19 (8), e0306492 , 2024
    2024
    Citations: 11
  • Identification and Categorization of Diabetic Retinopathy Using Modified Rider Optimization Algorithm
    D Nagadevi, N Chikyal, KR Reddy
    EAI International Conference on Advanced Technologies in Electronics … , 2024
    2024
  • BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images
    KR Reddy, KN Rajesh, R Dhuli, VR Kumar
    Frontiers in Human Neuroscience 18, 1405586 , 2024
    2024
    Citations: 10
  • A machine learning-based approach for the prediction of cardiovascular diseases
    KR Reddy, D Nagadevi
    MDPI , 2023
    2023
  • A machine learning-based approach for the prediction of cardiovascular diseases
    RR Kamireddy, N Darapureddy
    Engineering Proceedings 56 (1), 140 , 2023
    2023
    Citations: 3
  • Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor
    KR Reddy, R Dhuli
    The Journal of Supercomputing 79 (8), 9288-9319 , 2023
    2023
    Citations: 7
  • A new distinctive methodology for the classification of brain MR images using histogram based local feature descriptors
    K Sowjanya, KR Reddy, M Raveena
    International Journal of Computing and Digital Systems 13 (1), 1-1 , 2023
    2023
    Citations: 5
  • Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features
    KR Reddy, RK Batchu, S Polinati, DP Bavirisetti
    Frontiers in Human Neuroscience 17, 1157155 , 2023
    2023
    Citations: 5
  • A novel lightweight CNN architecture for the diagnosis of brain tumors using MR images
    KR Reddy, R Dhuli
    Diagnostics 13 (2), 312 , 2023
    2023
    Citations: 40
  • Image Defogging Algorithm Based on Fusion Approach
    CM Rao, P Srinivasulu, CA Sarma, KR Reddy
    2023
  • Segmentation and classification of brain tumors from MRI images based on adaptive mechanisms and ELDP feature descriptor
    KR Reddy, R Dhuli
    Biomedical Signal Processing and Control 76, 103704 , 2022
    2022
    Citations: 20
  • Face recognition based on local binary pattern-deep belief networks
    KN Prakash, KP Jasmine, KR Reddy
    Computer-Aided Developments: Electronics and Communication, 143-148 , 2019
    2019
  • Damaged video reconstruction using inpainting
    K Sonti, KR Reddy
    Computer-Aided Developments: Electronics and Communication, 201-207 , 2019
    2019
  • Design of Electronic Security System in Restricted Areas on MSP430 Processor
    K Rasool Reddy, D Vamsi Raju, G Kundan Kumar
    Microelectronics, Electromagnetics and Telecommunications: Proceedings of … , 2018
    2018
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Object detection and tracking--A survey
    KR Reddy, KH Priya, N Neelima
    2015 International conference on computational intelligence and … , 2015
    2015
    Citations: 54
  • A novel lightweight CNN architecture for the diagnosis of brain tumors using MR images
    KR Reddy, R Dhuli
    Diagnostics 13 (2), 312 , 2023
    2023
    Citations: 40
  • Segmentation and classification of brain tumors from MRI images based on adaptive mechanisms and ELDP feature descriptor
    KR Reddy, R Dhuli
    Biomedical Signal Processing and Control 76, 103704 , 2022
    2022
    Citations: 20
  • Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding
    RR Kamireddy, RN Kandala, R Dhuli, S Polinati, K Sonti, R Tadeusiewicz, ...
    PloS one 19 (8), e0306492 , 2024
    2024
    Citations: 11
  • BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images
    KR Reddy, KN Rajesh, R Dhuli, VR Kumar
    Frontiers in Human Neuroscience 18, 1405586 , 2024
    2024
    Citations: 10
  • Steganography based secret image sharing using block division technique
    M Tulasidasu, BL Sirisha, KR Reddy
    2015 International Conference on Computational Intelligence and … , 2015
    2015
    Citations: 9
  • Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor
    KR Reddy, R Dhuli
    The Journal of Supercomputing 79 (8), 9288-9319 , 2023
    2023
    Citations: 7
  • A new distinctive methodology for the classification of brain MR images using histogram based local feature descriptors
    K Sowjanya, KR Reddy, M Raveena
    International Journal of Computing and Digital Systems 13 (1), 1-1 , 2023
    2023
    Citations: 5
  • Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features
    KR Reddy, RK Batchu, S Polinati, DP Bavirisetti
    Frontiers in Human Neuroscience 17, 1157155 , 2023
    2023
    Citations: 5
  • GUI implementation of image encryption and decryption using Open CV-Python script on secured TFTP protocol
    KR Reddy, CM Rao
    AIP Conference Proceedings 1952 (1), 020074 , 2018
    2018
    Citations: 4
  • DBTNet: A Dual-Stream Neural Network for Effective Brain Tumor Detection in MRI Images
    RR Kamireddy, V Kadha, KN Rajesh, R Dhuli, S Hussain
    Arabian Journal for Science and Engineering 50 (23), 19789-19803 , 2025
    2025
    Citations: 3
  • A machine learning-based approach for the prediction of cardiovascular diseases
    RR Kamireddy, N Darapureddy
    Engineering Proceedings 56 (1), 140 , 2023
    2023
    Citations: 3
  • Detection of objects in cluttered scenes using matching technique
    KR Reddy, KVS Krishna, VR Kumar
    Int J Electron CommunTechnol 5 (3), 42-44 , 2014
    2014
    Citations: 3
  • Optimizing Brain Tumor Classification: Integrating Deep Learning and Machine Learning with Hyperparameter Tuning
    VK Velpula, KR Reddy, KN Prakash, KP Jasmine, V Jyothi Sri
    Engineering Proceedings 87 (1), 64 , 2025
    2025
    Citations: 2
  • Objective Quality Assessments of Restoration Images
    RR Kamireddy, S Punem, S Jangala, GRD Chamarthi, KY Srinivas
    Proceedings of International Conference on Communication and Networks … , 2017
    2017
    Citations: 2
  • Enhanced diabetes prediction using CTGAN-MLP approach on body composition data
    JH Joloudari, M Maftoun, MA Nematollahi, KN Rajesh, SP Vaidya, ...
    Scientific Reports , 2025
    2025
    Citations: 1
  • A Hybrid Framework for the Diagnosis of Parkinson’s Disease using Handwritten Drawings-Spiral and Wave
    KR Reddy, KN Rajesh, S Polinati, R Dhuli
    Journal of Scientific & Industrial Research 84, 520-530 , 2025
    2025
    Citations: 1
  • Mfernet: a deep cnn approach for detecting median filter tampering in re-compressed images
    V Kadha, KR Reddy, SK Das, M Mishra
    2024 IEEE 29th Asia Pacific Conference on Communications (APCC), 246-251 , 2024
    2024
    Citations: 1
  • Design of Electronic Security System in Restricted Areas on MSP430 Processor
    K Rasool Reddy, D Vamsi Raju, G Kundan Kumar
    Microelectronics, Electromagnetics and Telecommunications: Proceedings of … , 2018
    2018
    Citations: 1
  • Performance Assessment of Edge Preserving Filters
    KR Reddy, MR Ch, NR Kalikiri
    International Journal of Information System Modeling and Design (IJISMD) 8 … , 2017
    2017
    Citations: 1