• Manik
• Anil

Lung cancer is one of the leading causes of death related to cancer globally, and improving the chances of patient survival relies heavily on early detection. Among the various testing methods, chest x-ray imaging stands out as one of the most affordable and widely accessible options. Recently, machine learning (ML) techniques have emerged as powerful tools to enhance human understanding of the extent and classification of abnormalities in lung images captured by chest x-rays. This review provides an effective summary of several advanced ML techniques that have been used for the detection of lung cancer in chest radiographs. The paper illustrates selected variations of machine learning methods focusing on their performance, the datasets they utilize, and their preprocessing techniques. Common challenges faced in these studies, such as data imbalance, interpretability, and the applicability to actual clinical practice, are also discussed. Lastly, this review emphasizes future research gaps in ML techniques for lung cancer detection, highlighting the need for more robust AI, integrated machine learning approaches, and hybrid diagnostic systems.

Lung Cancer, Machine Learning, Chest X-ray, Deep Learning, Medical Image Analysis

"REVIEW ON MACHINE LEARNING APPROACHES FOR LUNG CANCER DETECTION USING CHEST X-RAY IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b677-b688, May-2025, Available :http://www.jetir.org/papers/JETIR2505178.pdf

"REVIEW ON MACHINE LEARNING APPROACHES FOR LUNG CANCER DETECTION USING CHEST X-RAY IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb677-b688, May-2025, Available at : http://www.jetir.org/papers/JETIR2505178.pdf