LUNG CANCER DETECTION AND CLASSIFICATION BASED ON DEEP LEARNING: A REVIEW - Ismaeel, Hivi Kamal; Abdulazeez, Adnan Mohsin (Universitas Teknokrat Indonesia, 2024)
AbstractLung cancer is a significant health problem worldwide because it is difficult to treat and often caused by factors such as smoking and lifestyle choices. Early detection and accurate classification are crucial for assisting patients. Lung cancer remains a major global health challenge due to its late detection and the complexity of its treatment options. Advancements in deep learning, a form of artificial intelligence that mimics the way humans learn, are offering new hopes for earlier detection and more accurate classification of this disease through the analysis of medical images. This review paper explores recent progress in the use of deep learning techniques, specifically focusing on how these methods are applied to improve lung cancer diagnostics. Our study delves into several types of neural networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), which have been adapted to analyze complex medical imaging data effectively. These networks help in identifying and classifying cancerous tissues from lung scans with a higher degree of accuracy than traditional methods, which rely heavily on human interpretation. We review a variety of models and approaches that demonstrate significant improvements in detecting lung cancer features from imaging studies like CT scans. These models not only enhance the accuracy but also reduce the time needed for diagnosis, which is crucial in improving patient outcomes. The paper discusses the specific roles of these models in automating the detection processes, their efficiency, and how they overcome some of the common challenges in lung cancer diagnosis, such as dealing with ambiguous or incomplete images. Furthermore, we address the challenges still facing deep learning applications in this field, including the need for large, annotated datasets and the computational demands of training complex models. Despite these challenges, the future looks promising due to the continuous improvements in computational power and the increasing availability of medical data.