Lung Disorder Identification by Optimized Deep Learning Model DOI

A. Agnes Pearly,

B. Karthik

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

HydraViT: Adaptive multi-branch transformer for multi-label disease classification from Chest X-ray images DOI
Şaban Öztürk, Mehmet Y. Turali, Tolga Çukur

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 106959 - 106959

Опубликована: Сен. 30, 2024

Язык: Английский

Процитировано

8

EO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19 DOI
Soner Kızıloluk, Eser Sert, Mohamed Hammad

и другие.

Journal of Applied Biomedicine, Год журнала: 2024, Номер 44(3), С. 635 - 650

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

5

PediaPulmoDx: Harnessing Cutting Edge Preprocessing and Explainable AI for Pediatric Chest X-ray Classification with DenseNet121 DOI Creative Commons

R. Priyanka,

G. Gajendran,

Salah Boulaaras

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104320 - 104320

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Lung Disorder Identification by Optimized Deep Learning Model DOI

A. Agnes Pearly,

B. Karthik

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

Процитировано

0