  Colon and Lung cancer is one of the most perilous and dangerous ailments that
individuals are enduring worldwide and has become a general medical problem. To
lessen the risk of death, a legitimate and early finding is particularly
required. In any case, it is a truly troublesome task that depends on the
experience of histopathologists. If a histologist is under-prepared it may even
hazard the life of a patient. As of late, deep learning has picked up energy,
and it is being valued in the analysis of Medical Imaging. This paper intends
to utilize and alter the current pre-trained CNN-based model to identify lung
and colon cancer utilizing histopathological images with better augmentation
techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16,
NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet,
and DenseNet169 are trained on LC25000 dataset. The model performances are
assessed on precision, recall, f1score, accuracy, and auroc score. The results
exhibit that all eight models accomplished noteworthy results ranging from 96%
to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture
the attention images of Pre-trained CNN models classifying malignant and benign
images.
