Supervised Machine Learning Approach for Lung Cancer Diagnosis

Abstract: This study assesses medical images, particularly Computed Tomography (CT) scans, for the early detection of lung cancer using processing the image, machine learning, and modern technology. The study highlights how raising patient survival rates depends on early-stage detection. Getting accurate standard performance is the primary goal. The methodology involves several processes, including dataset acquisition, data augmentation, pre-processing, selection of features, extraction of features, and CNN implementation. The outcomes of the trial indicate the precision with which our proposed technique works and how it could improve medical imaging in the existing clinical context for prevention and the therapy for lung cancer.

Keywords: Lung Cancer (LC), CT scan images, Convolutional Neural Networks (CNN)