Tuberculosis detection bars on VGG19 transfer learning and Zebra Optimization Algorithm

Tianzhi Le; Fanfeng Shi; Meng Ge; Ran Dong; Dan Shan

doi:10.4108/eetpht.10.5981

Authors

Tianzhi Le Yangzhou Polytechnic Institute
Fanfeng Shi Yangzhou Polytechnic Institute
Meng Ge Yangzhou Polytechnic Institute
Ran Dong Jiangsu Aodu intelligent Technology Company
Dan Shan Yangzhou Polytechnic Institute

DOI:

https://doi.org/10.4108/eetpht.10.5981

Keywords:

Tuberculosis Detection, Zebra Optimization Algorithm, Medical Image Analysis

Abstract

Tuberculosis (TB) remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. This study introduces a novel approach combining VGG19, a deep convolutional neural network model, with a newly developed Zebra Optimization Algorithm (ZOA) to enhance the accuracy of TB detection from chest X-ray images. The Zebra Optimization Algorithm, inspired by the social behavior of zebras, was applied to optimize the hyperparameters of the VGG19 model, aiming to improve the model's generalizability and detection performance. Our method was evaluated using a well-defined metric system that included accuracy, sensitivity, and specificity. Results indicate that the combination of VGG19 and ZOA significantly outperforms traditional methods, achieving a high accuracy rate, which underscores the potential of hybrid approaches in TB image analysis.

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