Artificial neural network for lung cancer detection using CT images

Abstract

Lung Cancer is the second most serious disease in today’s world due to which the mortality rate is increasing every year. Accurate and early identification of cancer in the lung could help people live longer. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. CT scan images provide the complete imaging of tumor development inside the lungs and are extensively used. A lung cancer diagnosis is usually done manually by skilled specialists, and while these approaches are particularly useful in advanced stage detection, it is also a time-consuming operation that is highly reliant on the person. This increases the risk of detecting inaccuracy due to human error procedures, necessitating the use of an automated system. As a result, it is necessary to identify lung cancer using an automated approach to decrease human manual error and enhance the accuracy and convenience of the process. In this proposed method image processing techniques and an artificial neural network (ANN) were employed to create an automatic method for accurate lung cancer detection. In the first step, image noise reduction, the histogram for image enhancement,