Deep Learning-Based Lung Cancer Classification Using TNM Coding: Insights for Pharmacological Interventions
Year: 2025
Type: article
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.
DOI: 10.14419/cvj7bm55
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4417036486
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