Published Paper Details:

Lung cancer remains a major global health concern, with early diagnosis playing a crucial role in enhancing patient survival rates. According to the Global Cancer Observatory (GLOBOCAN 2024), lung cancer remains the most common cause of cancer-related deaths worldwide, accounting for over 2.4 million new cases and 1.8 million deaths annually. The application of Artificial Intelligence (AI) in medical imaging has opened new avenues for improving lung cancer detection. This review examines the role of AI, particularly deep learning algorithms, in analysing medical images such as CT scans, X-rays, and MRIs for lung cancer diagnosis and prognosis. Various AI-based techniques, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and meta-heuristic approaches like the Crow Search Algorithm (CSA), have shown substantial progress in identifying and categorizing lung nodules as benign or malignant. Pre-processing steps such as image segmentation, edge enhancement, and resampling contribute to improving image clarity, thereby enhancing the accuracy of AI-driven diagnostic models. Despite these advancements, challenges such as data imbalance, model interpretability, and generalization persist. This paper also explores the potential of Computer-Aided Diagnosis (CAD) systems in complementing AI methodologies for more precise and reliable clinical applications. Additionally, the study reviews the limitations of conventional histopathological diagnostic techniques and the potential of molecular biomarkers in refining lung cancer classification. The growing use of AI in healthcare is paving the way for personalized treatment strategies, yet the necessity for diverse and extensive datasets remains critical for improving model reliability. Through this review, we aim to provide a structured overview of AI-driven medical imaging advancements in lung cancer detection, offering insights to guide future research and development.