Medical imaging is one of the most rewarding spaces to apply deep learning. Pathologists spend years learning to distinguish subtle visual patterns in tissue samples, and even then, fatigue and caseload pressure can creep into decisions. A well-trained CNN does not replace that expertise, but it can serve as a useful second opinion, especially in triage workflows. In this post we will walk through how we built a lung cancer image classifier that sorts tissue images into three classes: Adenocarcinoma , Benign , and Squamous Cell Carcinoma . The model runs behind a Flask API with a simple upload-and-predict web interface, so anyone can drop in an image and see the prediction in real time. Full code and dataset links are on GitHub . Why these three classes Lung cancer is commonly divided into small cell and non-small cell types. Within non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma are the two most prevalent subtypes, together making up the majority of cases.…