Detection of Non-small cell Lung Cancer using Histopathological Images by the approach of Deep Learning

“Lung cancer” is one of the most widely found cancers in the world, accounting for 2 million deaths in 2018 alone. It is still the leading cause of cancer worldwide. One of the most routine pathological diagnosis tasks for pathologists is the classification of cancer cells at the histopathological level. Histopathological images allow the pathologists to do an in-depth analysis of the cancer cells. A pathologist must evaluate the microscopic appearance of a “biopsied sample” based on morphological features that have been correlated with patient outcome in order to estimate the severity of a cancer. Since histopathological images provide a better understanding of the grade of the cancer, the dataset used in the articles are histopathological images. The model tries to harness the tremendous power of Artificial Intelligence to identify and classify lung cancer without the help of a pathologist. Knowing that pathologists are facing heavy workloads due to an increasing number of patients struggling with lung cancer, this model would be an appropriate fit for the medical industry. This model could also be used in regions that have a shortage of access to any Pathological center nearby. The output of our model will be the classification of the cancer image into malignant and benign cancer, and in the subsequent step, we hope we will be able to grade the cancer into its corresponding stage. The aim of the article is to do a comparative study between benign and malignant cancer cells.