Faculty Publications
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Item Smartphone Mammography for Breast Cancer Screening(Springer Science and Business Media Deutschland GmbH, 2021) Basu, R.; Madarkal, M.; Talukder, A.K.In 2020 alone approximately 2.3 million women were diagnosed with breast cancer which caused over 685,000 deaths worldwide. Breast cancer affects women in developing countries more severely than in developed country such that over 60% of deaths due to breast cancer occur in developing countries. Deaths due to breast cancer can be reduced significantly if it is diagnosed at an early stage. However, in developing countries cancer is often diagnosed when it is in the advanced stage due to limited medical resources available to women, lack of awareness, financial constraints as well as cultural stigma associated with traditional screening methods. Our paper aims to provide an alternative to women that is easily available to them, affordable, safe, non-invasive and can be self-administered. We propose the use of a smartphone’s inbuilt camera and flashlight for breast cancer screening before any signs or symptoms begin to appear. This is a novel approach as there is presently no device that can be used by women themselves without any supervision from a medical professional and uses a smartphone without any additional external devices for breast cancer screening. The smartphone mammography brings the screening facility to the user such that it can be used at the comfort and privacy of their homes without the need to travel long distances to hospitals or diagnostic centers. The theory of the system is that when visible light penetrates through the skin into the breast tissue, it reflects back differently in normal breast tissue as compared to tissue with anomalies. A phantom breast model, which mimics real human breast tissue, is used to develop the modality. We make use of computer vision and image processing techniques to analyze the difference between an image taken of a normal breast and that of one with irregularities in order to detect lumps in the breast tissue and also make some diagnosis on its size, density and the location. © 2021, Springer Nature Switzerland AG.Item A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Kumar, S.; Bhowmik, B.Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.Item Improving CNN-Based Breast Cancer Detection Integrating Quantum Layers(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Bhowmik, B.Breast cancer continues to be a significant burden on global healthcare systems, as early and accurate diagnosis is crucial for improving patient outcomes. Conventional methods used for diagnosis include mammography and biopsy; although they do supply critical information, they often have poor accuracy and are operator-dependent. Artificial Intelligence(AI), particularly Convolutional Neural Networks, presents a promising tool for analyzing medical images; however, conventional CNNs face significant challenges in generalizing from one dataset to another. This paper presents a hybrid Quantum Convolutional Neural Networks(QCNN) framework by integrating the classical feature extraction models VGG16, VGG19, and InceptionV3 with a Quantum Convolutional Layer (QCL). It uses the principles of quantum, such as superposition and entanglement, which process high-dimensional data for capturing non-linear patterns. Therefore, it improves the model's accuracy, sensitivity, and specificity. This hybrid framework presents a scalable and robust solution for the early detection of breast cancer, thereby advancing automated diagnostic systems to enhance reliability and adaptability. © 2025 IEEE.Item Crossover based technique for data augmentation(Elsevier Ireland Ltd, 2022) Raj, R.; Mathew, J.; Kannath, S.K.; Rajan, J.Background and Objective: Medical image classification problems are frequently constrained by the availability of datasets. “Data augmentation” has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results. Methods: This paper introduces “Crossover technique”, a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures. Results: The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture. Conclusion: The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation © 2022