Deep Learning in Cancer Diagnostics - A Feature-based Transfer Learning Evaluation (Paperback, 1st ed. 2023)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs in Applied Sciences and Technology
Release date:	2023
First published:	2024
Authors:	Mohd Hafiz Arzmi • Anwar P.P. Abdul Majeed • Rabiu Muazu Musa • Mohd Azraai Mohd Razman • Hong-Seng Gan • Ismail Mohd Khairuddin • Ahmad Fakhri Ab. Nasir
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	44
Edition:	1st ed. 2023
ISBN-13:	978-981-19-8936-0
Categories:	Books > Science & Mathematics > Physics > Applied physics & special topics > Biophysics Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Medicine > Clinical & internal medicine > Diseases & disorders > Oncology > General Promotions
LSN:	981-19-8936-2
Barcode:	9789811989360

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