Carcinoma Detection Using Deep Learning

The research work aims to improve the detection of cancer in CT scan images by employing advanced deep learning methodologies, namely Convolutional Neural Networks (CNNs), Visual Geometry Group-16 (VGG-16), and Visual Geometry Group-19 (VGG-19). The primary objective is to accurately classify CT scan images into four distinct categories: Adenocarcinoma, Large cell carcinoma, Squamous cell carcinoma, and normal lung tissue. In addition to model development, a user-friendly web interface is implemented using Flask, facilitating seamless interaction for clinicians and researchers. The interface allows for convenient uploading of CT scan images, which are then processed by the trained models to provide accurate carcinoma classification results. The significance of this research lies in its potential to revolutionize the field of radiology and oncology by offering efficient and accurate tools for early carcinoma detection and treatment planning. The developed methodologies pave the way for the integration of computer-aided diagnosis systems into clinical practice, ultimately leading to improved patient outcomes and healthcare delivery.