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Deep Learning for Tumor Classification: AI-Driven Diagnostic Decision-Making in Breast Cancer Imaging
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Author(s): Banashree Bondhopadhyay (Amity University, Noida, India), Navya Aggarwal (Amity University, Noida, India)and Shinjini Sen (Amity University, Noida, India)
Copyright: 2025
Pages: 24
Source title:
Cancer Diagnosis, Treatment and Care: Reflections for the Education of Survivors and Healthcare Providers
Source Author(s)/Editor(s): Karis L. Clarke (Touro University California, USA)and Noran L. Moffett (Fayetteville State University, USA)
DOI: 10.4018/979-8-3693-5400-1.ch010
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Abstract
This chapter addresses the urgent need for advanced diagnostic methods in breast cancer, which has become the most prevalent cancer among women globally. Radiological techniques such as Positron Emission Tomography - Computed Tomography (PET-CT) and Gamma Camera offer non-invasive, high-resolution imaging crucial for accurate diagnosis and staging. The increasing incidence of breast cancer underscores the demand for faster and more precise diagnostic tools, which Artificial Intelligence (AI) and Machine Learning (ML) can fulfil. This review explores the application of deep learning and neural networks within AI and ML frameworks to enhance the capabilities of radiologists in diagnosing, predicting prognosis, and guiding treatment decisions. Key methodologies including convolutional neural networks and autoencoders are detailed, demonstrating their role in improving the accuracy and efficiency of breast cancer detection and management.
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