Published Paper Details:

Lung tumor are responsible for a large number of death fatalities globally emphasizing the urgent need for new CAD tools for early identification and categorization this research automatically detects and categorizes lung cancer subtypes using automated CT scans using the most advanced InceptionV3 model a convolutional neural network to distinguish between adenocarcinoma, squamous and large cell carcinoma the model was trained on a variety of annotated CT scans allowing it to understand and recognize unique imaging features for each subtype. InceptionV3 architecture is designed to be highly precise and computationally efficient allowing lung pictures to be processed quickly by automating the diagnosis process. This research aims to improve patient outcomes and help healthcare providers make better treatment decisions and deployment of this system diagnostics could undergo a revolution if this approach is implemented successfully becoming more dependable and accessible while also relieving the strain on medical practitioners by displaying a better accuracy of 99%.