A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 14; p. 4928
• Main Authors: Shehata, Mohamed, Alksas, Ahmed, Abouelkheir, Rasha T., Elmahdy, Ahmed, Shaffie, Ahmed, Soliman, Ahmed, Ghazal, Mohammed, Abu Khalifeh, Hadil, Salim, Reem, Abdel Razek, Ahmed Abdel Khalek, Alghamdi, Norah Saleh, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.07.2021; MDPI
• Subjects: Accuracy; Adolescent; Adult; Aged; Aged, 80 and over; Angiomyolipoma; Carcinoma, Renal Cell - diagnostic imaging; CE-CT; Classification; Cost estimates; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; functionality; Humans; Investigations; Kidney cancer; Kidney Neoplasms - diagnostic imaging; Machine learning; Male; Middle Aged; morphology; Neural networks; Open source software; Public domain; RC-CAD; renal cell carcinoma; Support vector machines; texture; Tumors; Young Adult; United States > US; RC-CAD; renal cell carcinoma; functionality; morphology; CE-CT; texture
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21144928

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The i...