International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 4 · August 2026 · pp. 338-349
Research Article · Peer Reviewed
Received: May 16, 2026 · Accepted: July 23, 2026 · Published: August 11, 2026
DOI: 10.26706/ijceae.7.4.20260803
Open Access · CC BY 4.0

A Novel Hybrid InceptionV3-SqueezeNet Network (HIS-Net) Model for Kidney Disease Classification in CT Medical Image

Ruaa H. Ali Al-Mallah*
Department of Artificial Intelligence Engineering Technology, College of Technical Engineering for Computer and AI, Northern Technical University, Mosul 41000, Iraq.

*Corresponding author: ruaa_almallah@ntu.edu.iq
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Abstract

Accurate and early diagnosis of kidney diseases is essential for improving healing processes. This study proposes a novel hybrid deep learning model, called HIS-Net (Hybrid InceptionV3 - SqueezeNet), to enhance CT images classification of kidney disease on a dataset of 5,600 colorized samples across four categories: Tumor, Cyst, Stone, and Normal. A key novelty of this study is integrating parallel feature extraction by reducing dimensions of features. This improves efficiency and reduces computational complexity while preserving practical classification power. The framework incorporates preprocessing steps, including resizing, augmentation, normalization, grayscale conversion, and dataset partitioning. Two pre-trained CNNs, InceptionV3 and SqueezeNet, are employed in parallel as balancing feature extractors. Principal Component Analysis (PCA) is applied to each feature set, reducing them to 90 components, followed by feature fusion into a compact 180-dimensional representation. The fused features are evaluated using three machine learning classifiers, which are support vector machine (SVM), logic regression (LR), and naïve bayes (NB), under 10-fold cross-validation. The results show that grayscale images perform better than pseudo-color representations. SVM achieved the best performance with 98.5% accuracy and AUC of 1.000, with tumor sensitivity of 99.2%. These findings confirm that HIS-Net is an accurate, efficient, and reliable computer-aided diagnosis system.

Keywords

Hybrid deep network InceptionV3 SqueezeNet kidney disease medical image classification Support Vector Machine

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