Deep Learning Method for Lung Cancer Diagnosis and Nodule Classification Using Neural Networks

The research describes on developing an effective system for lung cancer identification and classification using Neural based Networks (CNNs). Lung cancer will stays as one of the leading issues of cancer-related deaths globally, and early time diagnosis will be crucial for improvising survival rates. This proposed study aims to leveraged the deep learning abilities of CNNs to automatically identify and differentiating lung related cancer from given medical related information. The proposed approach involves by training a neural structure to learn and identifying patterns that identifying malignant and benign lung nodules, improving the accuracy level of diagnosis. By automated the feature extraction process, the CNN - depending system minimizes the need for manual intervention, making it a valuable tool for radiologists. The system's ability to process large datasets and produce reliable outcomes offers a promising solution for earlier time lung cancer identification, potentially improvise patient outcomes and aiding in timely medical intervention.