Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers | ||||
| Arab Journal of Nuclear Sciences and Applications | ||||
| Volume 57, Issue 4, October 2024, Page 68-86 PDF (875.77 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ajnsa.2024.313417.1841 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ehab Helmy Elshazly  1; mohamed Kaloup2; Wessam S. ElAraby3
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| 1Assistant professor at Egyptian Atomic Energy Authority (EAEA), National Center for Radiation Research and Technology (NCRRT), Radiation Engineering Dept., | ||||
| 2associate professor at Egyptian Atomic Energy Authority (EAEA), National Center for Radiation Research and Technology (NCRRT), Radiation Engineering Dept., | ||||
| 3assistant professor at Radiation Engineering Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo 11787, Egypt. | ||||
| Abstract | ||||
| This research introduces an innovative method of Artificial Intelligence (AI) for improving the detection and classification of kidney diseases using CT images. The proposed method includes image preprocessing to remove artifacts, noise, and other image quality issues that can affect the accuracy of diagnosis. Then the area of interest in each image is segmented using Fractional Darwinian particle swarm optimization. Different features including Local Binary Pattern, Hu Moments, and Gray level zone length matrix (GLZLM) are extracted and fused using Canonical Correlation Analysis (CCA) and reduced using Two Dimensional Principal Component Analysis (2DPCA) to maintain only dominant features. A two-level classification approach is carried out to provide both fast and detailed diagnosis using both Binary Support Vector Machine (BSVM) and Convolutional Neural Network (CNN) in sequence. BSVM is used to initially discriminate between normal and kidney diseases categories. Afterwards, the detected abnormal kidney images are classified using CNN to different kidney diseases such as stones, cysts, and tumors. This approach aims to expedite the diagnostic procedure while also enhancing the efficiency and accuracy of classifying kidney disease in the clinical practice. Obtained results validate the efficiency of our proposed in terms of achieved accuracy when compared to alternative cutting-edge methods. | ||||
| Keywords | ||||
| Nephrolithiasis; Kidney Diseases; Convolution Neural Network; Medical CT Images; Support Vector Machine | ||||
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