Abstract
Abstract—Lung cancer is the leading cause of cancer related death in the world, and increased patient outcomes would lead to improvements in its diagnosis and detection at an early stage. Machine learning (ML) has emerged as a useful instrument in lung cancer diagnosis because it can offer superior diagnosis and aid in treatment decisions. Large clinical data sets, including patient demographics, medical histories, and genetic markers, can be analyzed by ML algorithms to find patterns and associations that can be used to estimate the risk of lung cancer. Numerous machine learning (ML) algorithms, such as logistic regression, support vector machines (SVMs), random forests, and deep learning models, have been used to predict lung cancer. Every method has advantages and disadvantages, and the best one to choose will rely on the particulars of the dataset as well as the intended result. Research has exhibited the effectiveness of machine learning (ML) in the prognosis of lung cancer, with a notable degree of accuracy in recognizing benign and malignant nodules on chest CT scans. This study utilizes deep learning techniques the Xception convolutional neural network model, to precisely classify different forms of lung cancer. To enhance the datasets diversity and boost our models training efficacy we apply data augmentation methods despite the encouraging outcomes, there are difficulties with using ML to predict lung cancer.However, it also comes with several challenges and shortcomings such as the need to select well-chosen and quality data, data bias issues, and the multifaceted decision-making that elaborate machine learning models entail.
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