Deep learning has shown great promise in the early detection and classification of lung cancer, a deadly disease with high mortality rates. This review explores the application of deep learning techniques, particularly Deep Convolutional Neural Networks (DCNN), in lung cancer diagnosis using various medical imaging modalities such as X-rays, Whole Slide Images (WSI), CT scans, and MRI. A systematic literature review (SLR) was conducted from 2015 to 2024, analyzing methodologies, recent advancements, quality assessments, and customized deep learning approaches. The study highlights the effectiveness of deep learning in overcoming the challenges of manually identifying and selecting features from lung cancer images. Convolutional Neural Networks (CNN), a popular deep learning method, demonstrated the highest accuracy due to its multi-layer structure, automatic weight learning, and ability to communicate local features. Performance metrics such as precision, accuracy, specificity, sensitivity, and AUC were used to evaluate various algorithms, with CNN consistently showing the best results. The findings emphasize the significant contributions of DCNN in improving lung cancer detection and classification, making them a valuable resource for researchers in medical applications. Lung cancer is a leading cause of death globally, with early detection crucial for improving survival rates. Factors such as tobacco use, air pollution, and chronic obstructive pulmonary disease (COPD) contribute to its prevalence. The study underscores the importance of deep learning in advancing the diagnosis and treatment of lung cancer.Deep learning has shown great promise in the early detection and classification of lung cancer, a deadly disease with high mortality rates. This review explores the application of deep learning techniques, particularly Deep Convolutional Neural Networks (DCNN), in lung cancer diagnosis using various medical imaging modalities such as X-rays, Whole Slide Images (WSI), CT scans, and MRI. A systematic literature review (SLR) was conducted from 2015 to 2024, analyzing methodologies, recent advancements, quality assessments, and customized deep learning approaches. The study highlights the effectiveness of deep learning in overcoming the challenges of manually identifying and selecting features from lung cancer images. Convolutional Neural Networks (CNN), a popular deep learning method, demonstrated the highest accuracy due to its multi-layer structure, automatic weight learning, and ability to communicate local features. Performance metrics such as precision, accuracy, specificity, sensitivity, and AUC were used to evaluate various algorithms, with CNN consistently showing the best results. The findings emphasize the significant contributions of DCNN in improving lung cancer detection and classification, making them a valuable resource for researchers in medical applications. Lung cancer is a leading cause of death globally, with early detection crucial for improving survival rates. Factors such as tobacco use, air pollution, and chronic obstructive pulmonary disease (COPD) contribute to its prevalence. The study underscores the importance of deep learning in advancing the diagnosis and treatment of lung cancer.