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), Random Forest (RF),
and k-Nearest Neighbors (KNN) 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.