Comparative Analysis of Artificial Intelligence for Predicting COVID-19 using Diverse Chest X-ray Images

Saleh, Radhwan A.A.; Al-Areqi, Farid; Al-Huda, Zaid; Al-antari, Mugahed A.

doi:10.1109/esmarta59349.2023.10293745

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Given the large size of our training dataset and the limits imposed by our devices, we did not need to focus on the more advanced tuning of the hyperparameters for using any other optimization algorithms. So, we used the same hyperparameters of our previous study, which used the Xception model to detect COVID-19 [23].…”

Section: Ai Model Trainingmentioning

confidence: 99%

AdvancingTire Safety: Explainable Artificial Intelligence-Powered Foreign Object Defect Detection with Xception Networks and Grad-CAM Interpretation

Saleh,

Al-Areqi,

Konyar

et al. 2024

Applied Sciences

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Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.

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Section: Ai Model Trainingmentioning

confidence: 99%