Abstract:
Cancer remains a formidable challenge to global healthcare, necessitating innovative approaches for early detection and personalized treatment. Genetic mutations are pivotal in cancer progression, demanding accurate and efficient detection methods. This thesis presents a "Deep Ensemble Learning Framework for Detection of Mutations to Detect Cancer Progression," harnessing the power of deep neural networks and ensemble techniques to address this critical issue. Traditional mutation detection methods, though valuable, are limited by their resource-intensive nature. Leveraging deep learning’s capabilities, our framework aims to enhance accuracy and scalability while automating the mutation detection process. By integrating diverse data modalities, including genetic sequences and histopathological images, we bridge the gap between cancer genetics and clinical practice.
