Multi cancer detection system

This research proposes a multi-cancer detection system that leverages the strengths of deep learning and classical machine learning techniques to classify breast cancer, lung cancer, and oral cancer from medical images.The system is designed to address the challenges of early cancer diagnosis by providing a scalable, efficient, and interpretable solution.It utilizes Convolutional Neural Networks (CNNs) for feature extraction, followed by classical machine learning algorithms such as Support Vector Machines (SVM), and Random Forest (RF) for classification.By combining the feature extraction capabilities of CNNs with the interpretability and robustness of classical machine learning models, the proposed framework offers a hybrid approach that enhances diagnostic accuracy and reliability.