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

  • Authors

    • Monali Chaudhari Assistant Professor, VES Institute of Technology, Mumbai, India
    • Tilottama Pankaj Dhake Assistant Professor, K. J. Somaiya Institute of Technology, Sion, India
    • Kanchan Mahajan Assistant Professor, Bharati Vidyapeeth's College of Engineering for Women, Pune, India
    • Diksha Parag Chopade Assistant Professor, Bharati Vidyapeeth's College of Engineering for Women, Pune, India
    • Pravin Gawande Assistant Professor, Vishwakarma Institute of Technology, Pune, India
    • Manish Rana Associate Professor, Department of Computer Engineering, Vidyaalankar Institute of Technology, Wadala( E), Mumbai, India
    • Altaf O. ‎ Mulani Professor, SKN Sinhgad College of Engineering, Korti, Pandharpur, India
    https://doi.org/10.14419/cvj7bm55

    Received date: April 25, 2025

    Accepted date: July 8, 2025

    Published date: December 4, 2025

  • Cancer Stage Prediction; CT Lung Scans; Lung Cancer Classification; Multilevel Deep Learning; Tumor Node Metastasis (TNM) Staging; ‎Pharmacological Interventions

  • Abstract

    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.

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  • How to Cite

    Chaudhari, M. . ., Dhake , T. P. ., Mahajan , K. ., Chopade , D. P. ., Gawande , P. ., Rana , M. ., & Mulani , A. O. ‎. (2025). Deep Learning-Based Lung Cancer Classification Using TNM‎Coding: Insights for Pharmacological Interventions. International Journal of Basic and Applied Sciences, 14(8), 63-70. https://doi.org/10.14419/cvj7bm55