Deep Learning-Based Lung Cancer Diagnosis and Classification Using CT Images

Abstract

One of the main causes of cancer-related fatalities globally is lung cancer, and increasing patient survival requires early diagnosis. Lung cancer screening frequently uses computed tomography (CT) imaging, although manual interpretation can be laborious and reliant on radiologist skill. A deep learning-based framework for CT image-based lung cancer diagnosis and classification is presented in this work. Lung CT scans are divided into three groups by the suggested system: benign, malignant, and normal. Preprocessing methods including Intensity-based feature enhancement, Contrast Limited Adaptive Histogram Equalization (CLAHE), and normalizing are used to improve image quality and highlight significant features. Both local texture features and global contextual information are extracted from CT images using a hybrid deep learning model that combines ResNet18 and Vision Transformer (ViT). Additionally, data augmentation is used to enhance model generalization and lessen class imbalance. The Hybrid-RViT model, which combined ResNet18 and ViT, outperformed the competition with 97.13% accuracy. These findings show that the suggested methodology can efficiently facilitate automated lung cancer diagnosis and help physicians make decisions more quickly and accurately.