Deep Learning in Cancer Diagnostics von Mohd Hafiz Arzmi | A Feature-based Transfer Learning Evaluation | ISBN 9789811989360

Deep Learning in Cancer Diagnostics

A Feature-based Transfer Learning Evaluation

von Mohd Hafiz Arzmi und weiteren
Mitwirkende
Autor / AutorinMohd Hafiz Arzmi
Autor / AutorinAnwar P. P. Abdul Majeed
Autor / AutorinRabiu Muazu Musa
Autor / AutorinMohd Azraai Mohd Razman
Autor / AutorinHong-Seng Gan
Autor / AutorinIsmail Mohd Khairuddin
Autor / AutorinAhmad Fakhri Ab. Nasir
Buchcover Deep Learning in Cancer Diagnostics | Mohd Hafiz Arzmi | EAN 9789811989360 | ISBN 981-19-8936-2 | ISBN 978-981-19-8936-0
“Each chapter includes a broad state-of-the-art section and compares the performances of several AI CAD approaches to the most common cancers using freely available datasets. … This book is intended for AI professionals and medical teams who are responsible for CAD approaches in healthcare settings, as well as researchers and PhD students in the areas of computer science (CS) engineering and medicine.” (Ramon Gonzalez Sanchez, Computing Reviews, October 4, 2023)

Deep Learning in Cancer Diagnostics

A Feature-based Transfer Learning Evaluation

von Mohd Hafiz Arzmi und weiteren
Mitwirkende
Autor / AutorinMohd Hafiz Arzmi
Autor / AutorinAnwar P. P. Abdul Majeed
Autor / AutorinRabiu Muazu Musa
Autor / AutorinMohd Azraai Mohd Razman
Autor / AutorinHong-Seng Gan
Autor / AutorinIsmail Mohd Khairuddin
Autor / AutorinAhmad Fakhri Ab. Nasir

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.