Novel Lung Cancer Classification and Segmentation Algorithm with Web UI Interface

For humans, it is difficult and time-consuming to use Computed Tomography (CT) pictures to identify lung illnesses. This research work develops a novel algorithm for segmenting the cancer part in the lung and an algorithm is enhanced to classify the lung cancer images. MediNet is a segmentation-based algorithm, used to segregate the cancer-affected region from an unaffected region in the CT image whereas LuNet is the algorithm developed by enhancing an algorithm to achieve the classification of the type of cancer. This MediNet algorithm involves layers such as thirty convolutional layers, three maxpooling layers, and three bi-directional convolutional layers. By obtaining 97% accuracy, MediNet outperformed other models. Besides this, a web application is also developed to assist the doctor with a UI interface. This is developed in such a way that the doctor can upload a cancerous or non-cancerous scan in the application which will be linked to the developed classification and segmentation algorithm Medinet and LuNet. Thebackend will be running in a server that will take the input from the web app and process the output and return back the results to the web application in terms of the presence or absence of cancer as well as segmented cancerous areas in case of presence of cancer.