Abstract
Cancer is the second-largest cause of death worldwide, constitute about one out of every six deaths. Diagnostic techniques have been developed for the early detection to determine benign and malignant ones and conduct appropriate treatment of the condition, which has led to a reduction in the incidence of death. Tumors can be detected and diagnosed by radiograph, magnetic resonance imaging, and ultrasound, and to confirm the type of tumor definitively, a biopsy is taken from the tumor, processed, and fixed on glass slides under a microscope and accurately identified. The explosive growth of artificial intelligence (AI) over the past ten years is the approved basis for making accurate decisions for diagnosing the type of tumor by building smart software based on machine learning (ML) and deep learning (DL), Which easier for specialists the access early detection of the type of tumor quickly. In this study, A study of previous works has been done for pathological conditions - breast, colon, and lung - which are the most common types of cancers, the accuracy of the diagnosis was studied for the type of tumor, benign or malignant , by using histological images by collecting biopsies from patients' tissues (histopathology) and characterizing them using the most recent convolutional neural networks (CNN), and researchers had to apply transfer learning techniques because Lack of explanations of the data histopathological dataset high-quality WSI (whole slice image), by training the network using a large computer vision data set (IMAGENET),in order to obtain a high diagnosis accuracy.
