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.