Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation (1st ed. 2023) (SpringerBriefs in Applied Sciences and Technology)

By: Mohd Hafiz Arzmi and Anwar P. P. Abdul Majeed and Rabiu Muazu Musa and Mohd Azraai Mohd Razman and Hong-Seng Gan and Ismail Mohd Khairuddin and Ahmad Fakhri Ab. Nasir

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Synopsis: Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.

Book Quality:: Publisher Quality
ISBN-13:: 9789811989377
Related ISBNs:: 9789811989360
Publisher:: Springer Nature Singapore
Date of Addition:: 02/19/23
Copyrighted By:: The Author
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Science, Computers and Internet, Technology, Medicine
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.