Concept AI driven early cancer detection of Skin, Breast and Lung cancer

This project focuses on the early detection of breast, lung, and skin cancers through medical image analysis, employing advanced deep learning techniques. The objective is to identify critical signs of cancer in the early stages, which can significantly enhance treatment outcomes. For breast cancer detection, the system classifies images into benign, malignant, or normal categories using CNN and VGG-16 models. In lung cancer detection, the system identifies three categories: adenocarcinoma, benign, and squamous carcinoma, utilizing CNN and MobileNet models. For skin cancer detection, the system classifies seven types of skin lesions, including actinic keratoses, basal cell carcinoma, and melanoma, using CNN and MobileNet models. The system integrates a user-friendly frontend developed with HTML, CSS, and JavaScript, while the backend is powered by Python and Flask. This approach leverages the power of convolutional neural networks (CNN) to analyze medical images for accurate and timely detection of cancer, providing an effective tool for healthcare professionals.

Keywords:

Breast cancer, lung cancer, skin cancer, CNN, VGG-16, MobileNet, early detection, medical image analysis, Flask, deep learning, cancer classification, benign, malignant, melanoma, adenocarcinoma, skin lesions, healthcare technology.