Comparative Analysis of Multiple Categorizations of Oncology Images for Cancer Cell Identification Using Parallel Transfer Learning Algorithms

Abstract

Cancer is a destructive, lethal, hazardous, and erratic disease. Predicting the condition, diagnosing it quickly and properly, and accurately predicting the prognosis are all necessary to lower the chance of mortality in this disease. As a disease, cancer has been found to have several distinct forms. As early cancer research has shown, it is essential to the medical treatment of patients to test for and follow a specific course of therapy for a certain cancer type at the earliest possible stage. Researchers from many different backgrounds have looked at how ML and Deep Learning techniques might be used in the fields of biology and bioinformatics to better categorize cancer patients into high- and low-risk groups. Algorithms from the fields of artificial intelligence (AI), machine learning (ML), and deep learning (DL) are already being put to good use in the healthcare system. A) is a simulation of human intellect that makes predictions using data, rules, and knowledge that has been put into it. In the realms of machine learning and artificial intelligence, deep learning (DL) has found widespread use in fields as diverse as healthcare and the development of new medicines. The prognosis for cancer is an assessment of the patient's likelihood of surviving the disease. Patients with cancer would benefit immensely from a prompt and precise diagnosis and prognosis. As a result of the widespread availability of powerful computers, DL has become the go-to method of data analysis. We investigate how AI aids in cancer diagnosis and prognosis, focusing on its remarkable accuracy, which is even greater than that of conventional statistical applications in oncology. AlexNet, GoogleNet, and, DenseNet, convolutional neural networks (CNNs are just some of the methods used in the advancement of forecasting models for predicting a cure for cancer. We show how these techniques contribute to the field's progress as well. The experiments are carried out on three different datasets, Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD), Digital Database for Screening Mammography's Curated Breast Imaging Subset (CBIS-DDSM), and Brain MRIs.