Machine Learning-Based Lung Cancer Classification and Enhanced Accuracy on CT Images

Lung cancer ranks as one of the main sources of death around the world.Because of the absence of symptoms in beginning phase patients, identifying and evaluating affected areas presents a significant challenge.Consequently, the mortality rate associated with lung cancer surpasses that of other lung diseases.Cellular breakdown in the lungs can be ordered into three sorts as Non-Small Cell Lung Cancer (NSCLC), Small Cell Lung Cancer (SCLC), and Carcinoid.Early detection is imperative, as it enables individuals to live longer lives.Computed Tomography (CT) scans are employed to locate tumors and determine the extent of cancer spread within the body.Early conclusion and characterization of cellular breakdown in the lungs are vital for working on a patient's possibilities of endurance, necessitating prompt lung disease detection.Accordingly, numerous machine learning and image processing models have been developed.This work efficiently classifies lung cancer as benign, malignant, or normal using a machine learning-based method for improved accuracy in lung cancer diagnosis on CT scans.The suggested model's accuracy on CT scans is increased by using the Random Forest algorithm to the detection of lung cancer.Metrics for accuracy, precision, sensitivity, and recall are used to assess the effectiveness of the approach that is being given.