Lung Cancer Segmentation and Detection using Machine Learning

Early detection of lung cancer is important in improving a patient’s life. Histopathological examination of tissue is a common procedure needed to obtain an early diagnosis. Tissue analysis is usually done by a pathologist review, however, this process is time-consuming and flawed. Early detection of cancerous regions will be greatly accelerated the whole process and help the pathologist. In this paper, we suggest a completely automatic way to get lung cancer throughout the slide images of lung tissue samples. Separation is done on image correction rate using a convolutional neural network (CNN). Two CNN architects (VGG and ResNet) are trained along with their own performance compared. The results obtained show that CNN the established method has the potential to assist pathologists in lung cancer diagnosis.