Comparison of deep transfer learning models for cancer diagnosis

Abstract

Cancer is known to be one of the most lethal diseases among all the diseases in the world. It is clinically known as ’Malignant Neoplasm which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. According to GLOBOCAN 2020, approximately 19.3 million new cases were found and around 10.0 million deaths have occurred for cancer globally. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. Breast, colorectal, endometrial, lung, oral, skin, and ovarian cancers are some of the most common malignancies that people develop. There are many medical procedures to identify the cancer cell such as mammography, MRI, CT scan which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of deep learning in medical image classification tasks, the recommended initiative targets to analyze the performance of deep transfer learning for cancer cell classification. Transfer learning is used in visual categorization to solve cross-domain learning issues by transferring useful data from the source domain to the task domain. Cancer, also known as tumor, must be discovered early and accurately in order to determine what treatment alternatives are available. Even if each modality has its own set of problems, such as a convoluted medical history, incorrect diagnosis, and therapy, all of which are major causes of death. Artificial Intelligence-based medical diagnosis is a novel strategy in medicine that eliminates the need for pathologists to work with material in favor of pixels to diagnose illness (imaging in medical sector). Therefore, in our paper, we want to offer a narrative of four different deep transfer learning techniques Vgg16, InceptionV3, MobilenetV2 and Resnet50 to examine the accuracy, compare and discuss for the detection of breast, lung, and melanoma (skin) cancer.