  Many incurable diseases prevalent across global societies stem from various
influences, including lifestyle choices, economic conditions, social factors,
and genetics. Research predominantly focuses on these diseases due to their
widespread nature, aiming to decrease mortality, enhance treatment options, and
improve healthcare standards. Among these, kidney disease stands out as a
particularly severe condition affecting men and women worldwide. Nonetheless,
there is a pressing need for continued research into innovative, early
diagnostic methods to develop more effective treatments for such diseases.
Recently, automatic diagnosis of Kidney Cancer has become an important
challenge especially when using deep learning (DL) due to the importance of
training medical datasets, which in most cases are difficult and expensive to
obtain. Furthermore, in most cases, algorithms require data from the same
domain and a powerful computer with efficient storage capacity. To overcome
this issue, a new type of learning known as transfer learning (TL) has been
proposed that can produce impressive results based on other different
pre-trained data. This paper presents, to the best of the authors' knowledge,
the first comprehensive survey of DL-based TL frameworks for kidney cancer
diagnosis. This is a strong contribution to help researchers understand the
current challenges and perspectives of this topic. Hence, the main limitations
and advantages of each framework are identified and detailed critical analyses
are provided. Looking ahead, the article identifies promising directions for
future research. Moving on, the discussion is concluded by reflecting on the
pivotal role of TL in the development of precision medicine and its effects on
clinical practice and research in oncology.
