Abstract
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
Highlights
Cancer is defined as abnormal cell growth that arises from any body organ
This paper presents an overview of Deep learning (DL) methods for the task of cancer diagnosis, prognosis, and prediction
The authors in [156] compared and contrasted the performance of different transfer learning architectures for the binary classification of brain tumors into benign and malignant categories. They chose AlexNet, GoogLeNet, ResNet-50, ResNet-101, and SqueezeNet architectures for comparison. They employed a dataset of 224 benign category and 472 malignant category T1-weighted Magnetic Resonance Imaging (MRI) images acquired from the The Cancer Imaging Archive (TCIA) public access repository
Summary
Further growth of the cells in these organs is saturated These silent and saturated cells are increased at a rapid rate till either their removal through a physical procedure such as surgery, medication, use of hormonal therapy, or radiation therapy or their disappearance on their own naturally. The natural disappearance of cancer cells can happen in cancers related to kidney or melanomas These cells can be screened using tools such as colonoscopy or pap smear examination or using mammograms. Apart from stem cells, WNT16B protein increases resistance against cancer along with chemotherapy. Therapies such as laser therapy and cryotherapy are some of the most vibrant approaches to treat cancer. Rare cancers such as osteosarcoma, Ewing’s sarcoma, male breast cancer, gastrointestinal stromal tumors, chondrosarcoma, mesothelioma, Computational and Mathematical Methods in Medicine adrenocortical carcinoma, cholangiocarcinoma, kidney chromophobe carcinoma, pheochromocytoma and paraganglioma, sarcoma, and ependymoma made up more than 20% of cancer cases and are rare types of cancers [1,2,3,4]
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