“A COMPREHENSIVE REVIEW OF DEEP LEARNING TECHNIQUES FOR LUNG CANCER DETECTION AND CLASSIFICATION”

Abstract

A crucial problem in medical imaging, the identification and categorization of lung cancer using computed tomography (CT) scans has significant consequences for early diagnosis and therapy. With an emphasis on different models and their performance evaluation, this study reviews and synthesizes recent advancements, spanning from traditional machine learning to advanced deep learning methods, for diagnosing lung cancer. The approaches cover a broad spectrum of methods, from conventional machine learning to advanced deep learning models and each brings a special strength to the field of lung cancer diagnosis. The combined results show that hybrid models, multi-scale learning, and 3D methods greatly improve detection robustness and accuracy. In order to increase model transparency and clinical acceptance, future research directions include integrating multi-modal data, improving data augmentation methods, and utilizing explainable AI.