Published Paper Details:

Lung cancer is the most common and deadliest cancer worldwide, where early detection is essential in improving patient outcomes. Machine learning (ML) has emerged as a groundbreaking healthcare technology with enormous potential in optimizing the accuracy, efficiency, and accessibility of lung cancer diagnosis. This paper explores various ML algorithms for the early detection of lung cancer from clinical and medical imaging data. Different approaches, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and ensemble models, are assessed based on their capacity to classify and predict malignancy in lung nodules [1] to [5]. The work utilizes public datasets such as Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) for training and validation models [6], [7]. Data preprocessing tasks like noise removal, feature extraction, segmentation, and increasing the quality and pertinence of the input data are performed [8]. The feature selection methods use dimensionality reduction techniques to ensure efficient performance and minimal computational cost [9]. Research has demonstrated that CNNs are more sensitive and specific for the detection of cancerous lesions than traditional ML approaches [10]-[12]. Deep learning algorithms are also more capable of detecting subtle imaging features that may not be detectable by the naked eye, and this improves the reliability of diagnosis. The addition of clinical parameters such as age, smoking status, and genetic predispositions improves predictive ability [13], [14]. In conclusion, ML use in lung cancer detection is a significant step toward early diagnosis, with high potential for enhanced mortality rates and personalized treatment planning.