Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Subject: search.filters.subject.Mammography

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.