Deep Learning-Based Lung Cancer Classification Using TNMCoding: Insights for Pharmacological Interventions

A multilevel deep learning approach known as TNM coding is commonly employed to classify lung cancer and predict disease stages. This ‎method integrates multiple deep learning networks to address complex challenges effectively. The objective of this study is to categorize CT ‎lung scans into three Tumor-Node-Metastasis (TNM) staging classes in accordance with the American Joint Committee on Cancer's (AJCC) ‎guidelines and to explore how accurate classification can support targeted pharmacological interventions. Initially, different lung diseases ‎, such as juxtapleural and internal nodules, are segmented automatically using an optimized conditional generative adversarial network (c-GAN). Following that, a combination of support vector machine classifiers and deep learning models is used to categorize tumors, nodes, ‎and metastases based on the AJCC staging criteria. This automated TNM cataloging method provides accurate cancer staging without ‎requiring manual detection of the region of interest (ROI) within CT scans. The proposed approach enhances the precision and cost-‎cost-effectiveness of CT scan analysis for lung cancer classification, thereby facilitating more tailored and effective pharmacological treatments‎.