Renal tumor classification and detection based on artificial intelligence

Raflaa Hilmi Al-taie; Nashwan J. Hussein

doi:10.1117/12.3058690

3 January 2025 Renal tumor classification and detection based on artificial intelligence

Raflaa Hilmi Al-taie, Nashwan J. Hussein

Author Affiliations +

Proceedings Volume 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024); 135190E (2025) https://doi.org/10.1117/12.3058690
Event: Third International Conference on Communications, Information System and Data Science 2024, 2024, Nanjing, China

Abstract

The kidney is a crucial organ in the human body, co-operating billions of pipelines to cleanse the body's water. Kidney failure, renal tumor happens when cells divide uncontrollably and form an aberrant collection of cells surrounding or within the kidney. This cell type has the ability to disrupt regular kidney activity and destroy healthy cells. The prompt diagnosis of renal tumor is critical since they can be fatal if left untreated. Because it is dependent on the skill of the person analyzing the images, the traditional approach of manually checking the MR image may not be very accurate. The study concentrated on the diagnosis of normal and normal renal tumor. To enhance accuracy and expedite diagnosis, using publicly accessible individual records, the present research used methodologies for machine learning, involving support vector machine learning (SVM), adaptive optimization (AO), as well as gradient enhancement (GE). It uses an approach for making the dataset reduced multidimensional. Image feature extraction is a data preprocessing method that minimizes the time required to train the proposed algorithm. The accuracy rates of the algorithms for diagnosing normal and up normal are reported to be 88.8 % for GB, 83.8 % for ADA, 86.1 % for SVM and 93.3% for KNN and for up normal diagnosis tumor are reported to be 55.3 % for GB, 55.4 % for SVM, 55.3 % for ADA and 51.0% for KNN.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Raflaa Hilmi Al-taie and Nashwan J. Hussein "Renal tumor classification and detection based on artificial intelligence", Proc. SPIE 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024), 135190E (3 January 2025); https://doi.org/10.1117/12.3058690

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KEYWORDS

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Magnetic resonance imaging

Feature extraction

Machine learning

Image processing

Kidney

Education and training

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