Deep Transfer Learning for Kidney Cancer Diagnosis
<|reference_start|>Deep Transfer Learning for Kidney Cancer Diagnosis: 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.<|reference_end|>
@article{habchi2024deep,
    title={Deep Transfer Learning for Kidney Cancer Diagnosis},
    author={Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou,
  Shadi Atalla, Wathiq Mansoor and Hussain Al-Ahmad},
    journal={arXiv preprint arXiv:2408.04318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04318},
    primaryClass={eess.IV cs.CV cs.LG}
}