@article{1454258,
      recid = {1454258},
      author = {Arzmi, Mohd Hafiz, and Majeed, Anwar P. P. Abdul, and  Muazu Musa, Rabiu, and Mohd Razman, Mohd Azraai, and Gan,  Hong-Seng, and Mohd Khairuddin, Ismail, and Nasir, Ahmad  Fakhri Ab.,},
      title = {Deep learning in cancer diagnostics : a feature-based  transfer learning evaluation /},
      pages = {1 online resource (44 pages) :},
      abstract = {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.},
      url = {http://library.usi.edu/record/1454258},
      doi = {https://doi.org/10.1007/978-981-19-8937-7},
}