IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 99 - 116

Опубликована: Апрель 4, 2025

Worldwide, lung disease is prevalent, so prompt diagnosis essential. For this reason, numerous machine learning and image processing models have been developed. This present study develops an interesting approach that uses convolutional neural networks (CNNs) optimized with the Bat-Grey Wolf Optimization Algorithm (BatGWO) to automatically detect diseases, particularly tuberculosis (TB) pneumonia. The proposed entails preprocessing dataset, which involves collecting modifying chest X-ray images from several public datasets derived Kaggle. CNN architecture's layers are then used for feature extraction, enables automatic of discriminative features suggestive disorders. Next, BatGWO optimize classifier, its performance compared other optimization algorithms, including PSO Bat (BA).

Язык: Английский