Deep Learning in Cancer Diagnostics (eBook)

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.

?

Dr. Mohd Hafiz Arzmi graduated with a Bachelor of Science (Microbiology) with honours from the University of Malaya, Malaysia. He obtained a Master's in Dental Science (MDSc.) from the Faculty of Dentistry, University of Malaya. He then received his PhD in Oral Microbiology and Immunology from Melbourne Dental School, the University of Melbourne, Australia. He is currently an Associate Professor at the Department of Fundamental Dental and Medical Sciences, Kulliyyah of Dentistry, International Islamic University Malaysia (IIUM). He is the Head of Cluster of Cancer Research Initiative IIUM (COCRII) and the coordinator for Oral Microbiology and Immunology, Kulliyyah of Dentistry, IIUM. Dr. Hafiz is a professional technologist (PTech), registered with the Malaysia Board of Technologists (MBOT), an executive member of the International Association of Dental Research (IADR), Malaysian Section and the executive member of the Malaysian Society for Oral Microbiologists and Oral Immunologists (MySOMOI).  His research interests are oral cancer, polymicrobial interactions, probiotics, and biofilms.

Dr Anwar P. P. Abdul Majeed graduated with a first-class honours B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an MSc. in Nuclear Engineering from Imperial College London, United Kingdom. He then received his PhD in Rehabilitation Robotics from the Universiti Malaysia Pahang (UMP). He is currently serving as an Associate Professor at the School of Robotics, XJTLU. Prior to joining XJTLU, he was a Senior Lecturer (Assistant Professor) and the Head of Programme (Bachelor of Manufacturing Engineering Technology (Industrial Automation)) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. He is also currently serving as an adjunct lecturer at UCSI University, Malaysia. Dr Anwar is also a Visiting Research Fellow at EUREKA Robotics Centre, Cardiff Metropolitan University, UK. Dr Anwar is a Chartered Engineer, registered with the Institution of Mechanical Engineers (IMechE), UK, a Member of the Institution of Engineering and Technology (IET), UK, as well as a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He is an active research member at the Innovative Manufacturing, Mechatronics and Sports Laboratory (iMAMS), UMP. His research interest includes rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis, machine vision as well as machine learning. He has authored over 60 papers in different journals, conference proceedings as well as books. He serves as a reviewer in a number of prolific journals, such as IEEE Access, Frontiers in Bioengineering and Biotechnology, SN Applied Sciences, PeerJ Computer Science, and Applied Computing and Informatics, amongst others. He has also served as a Guest Editor for SN Applied Sciences, MDPI, Frontiers, as well as an Editor for several Springer book series. He is currently serving as an Academic Editor for PLOS ONE, a Review Editor for Frontiers in Robotics and AI, an Associate Editor for Frontiers in Rehabilitation Sciences and a section editor for Mekatronika (UMP Press). Dr Anwar is also a member of the Young Scientists Network of the Academy of Sciences Malaysia (YSN - ASM). With regards to learned/civil society activities, he is an active member of the IET Malaysia Local Network as well as acting as a Liaison Officer for the Imperial College Alumni Association Malaysia.

Dr. Rabiu Muazu Musa holds a PhD degree in Sports Science from Universiti Sultan Zainal Abidin (UniSZA), Malaysia. He obtained his MSc in Sports Science from UniSZA and his BSc in Physical and Health Education at Bayero University Kano, Nigeria. His research activity focused on the development of multivariate and machine learning models for athletic performance. His research interests include sports performance analysis, health promotion, sport and exercise science, talent identification, test, and measurement as well as machine learning in sports. He is currently a senior lecturer at the Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu.

Dr. Mohd Azraai Mohd Razman graduated his first degree from the University of Sheffield, UK in Mechatronics Engineering. He then obtained his MEng. from Universiti Malaysia Pahang (UMP) in Mechatronics Engineering as well. He then completed his PhD at UMP specifically in the application of machine learning in aquaculture. His research interest includes optimization techniques, control systems, signal processing, instrumentation in aquaculture, sports engineering as well as machine learning.

Dr. Gan Hong Seng holds a Ph.D. Degree in Biomedical Engineering from Universiti Teknologi Malaysia (UTM). He obtained his B.Eng. in Biomedical from UTM. Currently, Dr. Gan is a faculty member of the School of AI and Advanced Computing, Xi' an Jiaotong - Liverpool University. His research activities focus on the development of machine learning models for medical imaging tasks. His research interests include medical image processing, deep learning, computer vision and artificial intelligence.

Dr. Ismail Mohd Khairuddin is a senior lecturer at Universiti Malaysia Pahang. He received his Bachelor's Degree in Mechatronics Engineering from Universiti Teknikal Malaysia Melaka (UTeM) in 2010 and was awarded with a Master's Degree in Mechatronics and Automatic Control from Universiti Teknologi Malaysia in 2012. Then, obtained his Ph.D. in Biomechatronics Engineering at the International Islamic University Malaysia. His research interests include rehabilitation robotics, mechanical and mechatronics design, mechanisms, control and automation, bio-signal processing as well as machine learning.

Dr. Ahmad Fakhri bin Ab. Nasir received his Bachelor's Degree in Information Technology from Universiti Malaya. He enrolled as a full-time master's student at the Faculty of Manufacturing Engineering, Universiti Malaysia Pahang (UMP), and received his Master's Degree in Engineering (Manufacturing). He then pursued his Ph.D. specialising in Pattern Recognition at the Universiti Sultan Zainal Abidin. He joined Universiti Malaysia Pahang as a senior lecturer in the midst of 2016. Before serving at the Faculty of Computing, UMP, he served at the Faculty of Manufacturing Engineering, UMP. His research interests are in the areas of computer vision, pattern recognition, image processing, machine learning, as well as parallel computing.

?

---

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.?