Rabiu Muazu Musa

Anwar P. P. Abdul Majeed

Mohd Azraai Mohd Razman

Ahmad Fakhri Ab. Nasir

Mohd Hafiz Arzmi

Hong-Seng Gan

Ismail Mohd Khairuddin

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. ​

A Feature-based Transfer Learning Evaluation

Deep Learning in Cancer Diagnostics

Muhammad Aizzat Zakaria

Ahmad Najmuddin Ibrahim

Ismayuzri Ishak

Nafrizuan Mat Yahya

This book presents part of the iM3F 2020 proceedings from the Mechatronics track. It highlights key challenges and recent trends in mechatronics engineering and technology that are non-trivial in the age of Industry 4.0. It discusses traditional as well as modern solutions that are employed in the multitude spectra of mechatronics-based applications. The readers are expected to gain an insightful view on the current trends, issues, mitigating factors as well as solutions from this book.

Selected Articles from iM3F 2020, Malaysia

Recent Trends in Mechatronics Towards Industry 4.0

Ahmad Fakhri Ab. Nasir

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