International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 2 · June 2026 · pp. 211-226
Research Article · Peer Reviewed
Received: March 15, 2026 · Accepted: May 31, 2026 · Published: June 10, 2026
DOI: 10.26706/ijceae.7.2.20260310
Open Access · CC BY 4.0

Hybrid ResNet50 and DenseNet121 with Adaptive Weighted Feature Fusion in Infectious Chest Diseases Classification

Samaa Khalid Ghanim*
Department of Engineering Electronic and Communication Techniques, Polytechnic College Mosul, Northern Technical University, Iraq.

*Corresponding author: mti.lec22.samaa@ntu.edu.iq
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Abstract

The usage of image classification has importance since it permits for prompt and accurate diagnosis based on medical images corresponding X-rays in detecting infections of chest. The early detection of infection helps to treat and prevent from diseases spread. In addition, it supports health care practitioners to be efficient and preventing errors in diagnosis procedure. In this paper, four classes of infectious chest diseases (COVID-19, Normal, Pneumonia and Tuberculosis) are classified using a proposed deep learning based on new method of Adaptive Weighted Feature Fusion (AWFF) that combines the strengths of both ResNet50 and DenseNet121 into balance database. The fusion approach allows a model to take deep features and reuse of robust feature. The results show that the proposed model achieves high performance classification reach to accuracy 98.63%, precision = 98.72%, recall = 98.84% and F1-score = 98.75%, indicating its strength in classifying infectious chest diseases.

Keywords

ResNet50 DenseNet121 Adaptive Weighted Feature Fusion Infectious Chest Diseases Classification Convolutional Neural Network Image Classification

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