A Multi-Modality Approach to Breast Cancer Diagnosis: Fusing Ultrasound with Other Imaging Techniques Syed Rizwana, Shaik Inthiyaz, Ala Lakshmi Priyanka, Tirunavalli Mohana, Shaik Khaja Mohiddin Basha, Dodda Venkatareddy 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025 It is the most crucial health issue related to mortality rates among females globally. In relation to that, there have been recent developments in AI and ML to serve better the diagnosis process of breast cancer. The present study hence forth proposed a new way of analzsing the breast using ultrasound imaging with transfer learning and ensemble methods. It integrates cutting edge transfer learning models with advanced ensemble techniques based on Multi-Layer Perceptron’s and Support Vector Machines with different kernels. Finally, this system is evaluated, which provides a classification accuracy of 90% and an overall accuracy of 90% with evidence of significant i m provement o v er existing diagnostic systems. It offers a structured procedure covering all steps from handling the input data up to model training and performance evaluation; it, therefore seems to be particularly effective for diagnostics enhancement related to breast cancer.
Computer Aided Detection of Breast Cancer Using Bio Inspired Algorithm Syed Rizwana, Peddi Kavya, Bolla Vinay Pooja, Bandi Poojitha, Shaik Khaja Mohiddin Basha, Dodda Venkatareddy 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025 Breast cancer still ranks among the most common causes of cancer-related deaths among women, hence the call for early diagnosis. Mammography is the most accepted screening test, but conventional computer-aided detection (CAD) has a high false positive rate (FPR) that gives rise to biopsy and false negatives (FN) where cancer is undetected. In solving these challenges, this paper provides a solution by employing the use of the Simple Genetic Algorithm (SGA), which is openly inspired by biological systems to enhance the performance of CAD systems for breast cancer detection. The SGA, which is based on the evolutionary process, can resolve problems in feature selection and classification o f t he m ammogram b y overcoming shortcomings of pattern recognition. By mimicking the genetic evolution process, ant colony optimization, and swarm intelligence, the SGA prevents noisy or variant images from anyhow decreasing the detection accuracy. Comprehensive tests on typical sets of mammograms confirm t he e ffectiveness o f t he proposed approach regarding a twofold reduction of inappropriate positive and negative results. This enhanced accuracy of diagnoses can help radiologists to act early, combined with favorable outcomes for the patients, implying that early diagnosis may save lives.
DeepWaterSeg: High-Resolution Satellite Imagery Analysis for Water Body Extraction Using ResNet-U-Net Shaik.Khaja Mohiddin Basha, Ramisetty Anjaneya Kumar, Komaragiri Durga Prasad, Syed Matheen Baba, Shaik Peer Mohammad Shaahid, Mallikarjuna Rao Gundavarapu, Sireesha Moturi 2025 IEEE 6th Global Conference for Advancement in Technology Gcat 2025, 2025 Accurate identification of water bodies is an important aspect of ecological oversight, disaster management, resource allocation, and conservation of aquatic life. To address this, the current research work DeepWaterSeg presents a deep learning technique for accurate identification of water bodies from high-resolution satellite images, including ponds, lakes, rivers, and more across diverse landscapes such as mountains, farmlands, and urban areas.This research utilizes a ResNet50-based U-Net architecture trained on high-resolution satellite imagery, which enables precise mapping of water bodies. The model achieved impressive results with 96% pixel-wise precision and 92% accuracy, making it highly effective for segmentation tasks.The new DeepWaterSeg model works better than DMLU-Net and SDNet, which are both recent models. It gets 96% pixel-wise accuracy and 90% IoU on a variety of landscapes while being less computationally complex. This high level of accuracy makes it possible to reliably find both narrow and urban water bodies, showing that it could be useful for disaster response, resource management, and long-term ecosystem monitoring.The proposed methodology can significantly aid in large-scale, real-time monitoring, contributing to improved disaster preparedness, drought assessment, and sustainable water resource utilization.
Leveraging Deep Learning for Enhanced Pneumonia Detection in Chest X-Rays Shaik Khaja Mohiddin Basha, Pogula Sai Sri Varsha, Sankuru Sai Latha, Vemula Sireesha, Syed Rizwana 2024 1st International Conference on Innovations in Communications Electrical and Computer Engineering Icicec 2024, 2024 Early detection of pneumonia and acute respiratory infections caused by viruses and bacteria is critical for effective treatment and prevention of serious complications. Advanced technologies, especially AI and Deep learning, have proven valuable in enhancing this process. Our study introduces the Deep CNN (Convolutional Neural Networks) Algorithm, a modified deep convolutional neural network A model using chest X-rays to predict pneumonia in infected groups and uninfected groups by preprocessing. Using a dataset of 5,855 segmented images enhanced by data augmentation techniques, Deep CNN achieved a high classification accuracy of 91.46% for unseen images. This performance outperforms traditional models such as ImageNet, DenseNet, and VGG16, providing a model for accurate diagnosis of lung disease. Demonstrating high potential, the Deep CNN model represents a major advance in medical imaging technology, providing a reliable tool for early diagnosis and guiding treatment strategies. Its high accuracy highlights the potential of AI-driven processes to transform healthcare, improve patient outcomes, and support more effective and timely treatment.
Early Diagnosis of Lung Cancer using Hyperparameter-Tuned Machine Learning Models Shaik Khaja Mohiddin Basha, Syed Rizwana, Gottimukkala Sasank Chandra, Gudi Manikanta, Bukya Venkata Sai Durga Naik, Venkata Reddy Dodda 2nd IEEE International Conference on Integrated Intelligence and Communication Systems Iciics 2024, 2024 Globally, lung cancer is the leading cause of cancer-related deaths. Improving survival depends on early and precise identification, which is still mostly accomplished using antiquated techniques that are frequently ineffective and erroneous. In this study, we used patient demographic and clinical data to create a machine learning-based risk prediction model that aims to diagnose lung cancer early and accurately. To verify the performance on our lung cancer data set, we employed important machine learning algorithms such as logistic regression, random forest, and support vector machines (SVM). PCA for feature selection, resampling to balance classes, and handling missing values were all examples of preprocessing. Among models, Random Forest shown excellent efficiency in risk detection, while SVM ultimately.
Intelligent Heart Rate Classification with Adaptive Neuro-Fuzzy Inference System Approach Banothu Seva, Bhupchand Kumhar, Jyoti Gupta, Sharda Patel, Shaik Khaja Mohiddin Basha, Archi Jain 2024 IEEE 4th International Conference on ICT in Business Industry and Government Ictbig 2024, 2024 The human heart is considered among the most crucial internal organs found in the body. An ECG signal is defined as the widespread name given to any electrical signal that is generated by the circulatory system*. By using the ECG signal, one can estimate anomalies present in a heart. This study is aimed at grouping the electrocardiogram (ECG) signals, and thereby using an adaptive neuro fuzzy inference system (ANFIS) algorithm. In this study, the ANFIS which is associated with the back propagation approach is used in order to perform the categorising task. ANFIS can be developed from the integration of fuzzy logic which is a qualitative technique with the neural network aspect in allowing adaptability. Attribute selection is done before the classification exercise is conducted. Four different types of ECG beats can be retrieved from the datasets available at PhysioBank. Classification of the cardiac signals is done employing four ANFIS classifiers. The fifth ANFIS decoder is used to get a higher discrimination ratio in order to classify the electrocardiograms accurately.
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