Deep Learning for Tumor Classification
Banashree Bondhopadhyay, Navya Aggarwal, Shinjini Sen · 2025
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.