International Journal of Emerging Research in Engineering, Science, and Management
Vol. 5, Issue 2, pp. 01-22, Apr-Jun 2026.
https://doi.org/10.58482/ijeresm.v5i2.1

Received: 28 Dec 2025 | Revised: 06 Apr 2026 | Accepted: 18 Apr 2026 | Published: 26 Apr 2026

This work is licensed under a Creative Commons Attribution 4.0 International License.

Enhanced Prediction of Chronic Kidney Disease Based on an Integrated Framework of High-Dimensional Medical Image Data and Deep Learning

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1M. Naresh

2M. V. Nageswara Rao

3D. Venkat Reddy

4Koiloth SRS Jyothsna

4Namburi Dhana Lakshmi

5Nanduri Srinivas

1Associate Professor, Department of ECE, Matrusri Engineering College, Saidabad, Hyderabad, India.

2Associate Dean-Academics, GMR Institute of Technology, Razam, Andhra Pradesh 532127, India.

3Professor, Department of ECE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, Telangana – 500075, India.

4Assistant Professor, Department of ECE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana – 500075, India.

5Associate professor, Department of Mathematics, R K College of Engineering, Kethana Konda, NTR District., Andhra Pradesh, India.

Abstract: Chronic kidney disease (CKD) is characterized by progressive kidney damage and is a rapidly growing global health concern. CKD significantly increases morbidity and mortality risk. One way to lower the CKD mortality rate is to diagnose and treat patients early. Early detection of CKD remains challenging for medical practitioners. By integrating state-of-the-art deep-learning (DL) methods with high-dimensional medical image data, the proposed model enhances CKD prediction while addressing challenges related to data quality and scalability. Pre-processing, feature extraction, feature selection, and classification are the four stages of the proposed framework. The three models that extract the essential features are Inception-V3, ResNet-50, and VGG-16. In order to choose the best features and minimize problems caused by feature dimensionality, an upgraded version of the particle swarm optimization method (UPSO) is introduced during the feature selection step. Then, a deep capsule model combined with a depth-wise convolutional network is used for classification. The last step in improving CKD prediction is hyperparameter optimization, namely the Crayfish Optimization Algorithm (COA). Using performance metrics such as accuracy, precision, recall, F1-score, and specificity, the proposed technique is validated using benchmark CT kidney and retinal image datasets. The proposed model achieves an accuracy of 99.72% by integrating hybrid MMG–CLAHE preprocessing, multi-backbone transfer learning feature extraction (Inception-V3, ResNet-50, VGG-16), UPSO-based feature selection, and a depth-wise convolutional capsule classifier optimized using COA for CKD prediction.

Keywords: Chronic kidney disease, Deep learning, Medical image analysis, Transfer learning, Capsule network, Crayfish optimization algorithm.

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This article is part of Volume 5, Issue 2 (Apr–Jun 2026)

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