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    PERFORMANCE OF DEEP RESIDUAL NETWORKS IN LUNG CANCER CLASSIFICATION: AN ANALYSIS ON HISTOPATHOLOGICAL IMAGES
    (Sivas Cumhuriyet Üniversitesi, 2024) Yağmurcu, Merve; Uzun, Sultan; Polat, Özlem
    Lung cancer is one of the most commonly seen and deadly types of cancer worldwide. Early diagnosis of this disease is crucial for prolonging life and improving treatment success. This study focuses on classifying lung cancer from histopathological images and investigates the performance of residual-based models (ResNet18, ResNet34, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2) in classification. The LC25000 dataset, containing three classes—adenocarcinoma, benign, and squamous cell carcinoma—with 5000 images per class, was used. Among the tested models, ResNet18 achieved the highest classification performance with an accuracy of 99.90%. The results demonstrate that ResNet-based models perform excellently in accurately classifying complex histopathological images and highlight the potential of deep learning methods as a practical solution for lung cancer diagnosis.