Deep Learning-Driven Detection of Lung and Colon Cancer via InceptionV3 Architecture

Spotting cancer early and plays a key role in boosting patient results and how well treatments work. This project puts forward a solution based on deep learning to sort lung and colon cancer by looking at tissue images under a microscope. The heart of the system is an InceptionV3 convolutional neural network that's been tweaked and trained with PyTorch to spot five specific types: Lung Squamous Cell Carcinoma, Small Cell Lung Carcinoma, Lung Adenocarcinoma, Colon Tissue, and Colon Adenocarcinoma. Before the model sees the images, they get resized and normalized to match the training data. The system also has a simple login setup to keep user access safe. This project aims to show how AI can lend a hand in medicine by offering reliable image-based diagnoses helping doctors make quicker and more accurate calls.