Comparative analysis of the KCDC-SODLPI technique with recent approaches [15].

Comparative analysis of the KCDC-SODLPI technique with recent approaches [15].

<p>Comparative analysis of the KCDC-SODLPI technique with recent approaches [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0326080#pone.0326080.ref015" target="_blank">15</a>].</p>

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Bibliographic Details
Main Author:	Wafa Almukadi (21722188) (author)
Other Authors:	Sayed Abdel-Khalek (21722191) (author), Adel A. Bahaddad (14593230) (author), Ahmed Mohammed Alghamdi (17817219) (author)
Published:	2025
Subjects:	Cell Biology Biotechnology Marine Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sensitive medical task recognizing complex patterns extracting intricate patterns driven early detection biomedical image dataset automated techniques powered superior accuracy value renal cell carcinoma reliable diagnostic processes analyzing histopathological images advanced computational methods kidney cancer detection classify kidney cancer chronic kidney cancer incorporating deep learning sodlpi method portrayed sodlpi method employs sodlpi technique utilizes dl methods showcase kidney cancer deep learning diagnostic method sodlpi method renal oncology increasing accuracy densenet method sodlpi technique pathological images input images xlink "> timely identification study presents snake optimizer significant cause recent times promising solution notable capability noise content multifaceted process main aim inherent complexity gaussian filtering experimental validation demanding requirements consuming aspect classification employing breakthrough alteration anomalies within also paves
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