Integrated Approaches for Efficient Multi-Modal Breast Cancer Detection

Registration ID: IJNRD_221592

Published ID: IJNRD2406386

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Vanshika Pandey, Anuj Gupta, Aman Sahi, Sanat Sharma

Title: Enhancing Breast Cancer Detection: Integrated Approaches and Future Directions Breast cancer remains a significant health concern globally, necessitating the development of innovative approaches for its early detection. This paper proposes integrated methodologies for enhancing the efficiency of multi-modal breast cancer detection. Traditional mammography, while effective in identifying tumors, has limitations in detecting early-stage cancers. To address this challenge, we employ transfer learning techniques to leverage the discriminative power of mammography images and extend it to ultrasound and thermography modalities. By training a model initially on mammography images, we aim to capitalize on its capability to detect subtle abnormalities indicative of early stage tumors. Subsequently, the pre-trained model is adapted to ultrasound and thermography images, thereby enhancing the sensitivity and accuracy of cancer detection across multiple imaging modalities. Through this integrated approach, we seek to improve the efficacy of breast cancer screening, particularly in identifying tumors at their nascent stages, facilitating timely intervention and improved patient outcomes. Looking ahead, projections indicate a staggering 70% increase in the number of new breast cancer cases over the next two decades. This forecast emphasizes the critical importance of early and precise diagnosis in improving patient prognosis and elevating survival rates, which currently hover at a modest 30 to 50%. Breast tumours are broadly classified into benign and malignant subtypes, each necessitating tailored diagnostic and treatment approaches due to variations in prognosis and therapeutic response. In recent years, there has been a surge in interest and investment in leveraging emerging technologies, such as deep learning and medical imaging, to revolutionize breast cancer detection. These technologies hold the promise of enhancing accuracy, efficiency, and patient experience in screening and diagnosis. Our project seeks to harness the potential of these advancements by developing a comprehensive breast cancer detection solution that integrates multiple imaging modalities and state-of-the-art machine learning techniques. v Traditional diagnostic modalities primarily rely on medical imaging techniques such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and histopathology (HP) images. However, manual analysis of HP images presents significant 3 challenges, including a scarcity of expert pathologists, time-intensive analysis procedures leading to fatigue and reduced attention, and reliance on individual pathologists' expertise, which may result in misdiagnosis, particularly in the early stages of breast cancer. The need for more sensitive and efficient breast cancer detection methodologies has spurred the exploration of alternative imaging modalities, such as ultrasound and thermography. While these modalities offer complementary information to mammography, they often lack the sensitivity to detect early-stage tumors independently. Hence, there is a pressing need to develop integrated approaches that leverage the strengths of each modality to enhance overall detection accuracy. In this context, our project aims to address the limitations of individual imaging modalities by proposing integrated approaches for efficient multi-modal breast cancer detection. We begin by utilizing state-of-theart deep learning architectures, including Convolutional Neural Networks (CNNs), ResNet, and Vision Transformers, to analyze mammography images. These architectures have demonstrated remarkable success in image classification tasks and are well-suited for identifying intricate patterns indicative of cancerous lesions. Transfer learning in our case entails training the model initially on mammography images and subsequently transferring its knowledge to ultrasound and thermography images. By fine-tuning the pre-trained model on these alternative modalities, we aim to exploit the shared underlying features of breast pathology across different imaging techniques. This approach capitalizes on the ability of mammography to highlight subtle abnormalities indicative of early-stage tumors, leveraging this knowledge to enhance the sensitivity of ultrasound and thermography 4 in detecting such lesions. Through the transfer of learned representations from mammography to other modalities, we seek to augment the diagnostic capabilities of each imaging technique, thereby improving the overall accuracy and reliability of breast cancer detection.

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Vanshika Pandey, Anuj Gupta, Aman Sahi, & Sanat Sharma (June-2024). Integrated Approaches for Efficient Multi-Modal Breast Cancer Detection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), d937-d941. https://ijnrd.org/papers/IJNRD2406386.pdf

Volume 9 Issue 6, June-2024

Pages : d937-d941

Paper Reg. ID: IJNRD_221592

Published Paper Id: IJNRD2406386

Research Area: Bio Medical Engineering 

Author Type: Indian Author

Country: Greater Noida, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2406386.pdf

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