Early and precise identification of monkeypox is crucial for controlling outbreaks and reducing the spread of this new infectious disease. However, existing deep learning-based diagnostic models face substantial obstacles due to a lack of diverse datasets and model explainability, both of which are critical for clinical adoption. In order to solve these problems, this research first proposes a way to increase dataset diversity by combining two frequently used datasets. This integrated dataset enhances the generalization capabilities of deep learning models by providing a more comprehensive representation of monkeypox cases. Second, four deep learning models—Vision Transformer (ViT), ConvMixer, Xception, and AlexNet—tuned are thoroughly evaluated for monkeypox detection. The Gradient-weighted Class Activation Mapping (Grad-CAM) method, which offers visual insights into each model’s decision-making processes, is utilized to ensure the models’ transparency and interpretability. The results demonstrate that combining the two datasets and integrating explainability into AI models increase diagnostic accuracy and offer important justifications for the model’s predictions, hence boosting confidence in diagnoses driven by AI.

Abstract Visual inspection of defective tires post-production is vital for human safety, as faulty tires can lead to explosions, accidents, and loss of life. With the advancement of technology, transfer learning (TL) plays an influential role in many computer vision applications, including the tire defect detection problem. However, automatic tire defect detection is difficult for two reasons. The first is the presence of complex anisotropic multi-textured rubber layers. Second, there is no standard tire X-ray image dataset to use for defect detection. In this study, a TL-based tire defect detection model is proposed using a new dataset from a global tire company. First, we collected and labeled the dataset consisting of 3366 X-ray images of faulty tires and 20,000 images of qualified tires. Although the dataset covers 15 types of defects arising from different design patterns, our primary focus is on binary classification to detect the presence or absence of defects. This challenging dataset was split into 70, 15, and 15% for training, validation, and testing, respectively. Then, nine common pre-trained models were fine-tuned, trained, and tested on the proposed dataset. These models are Xception, InceptionV3, VGG16, VGG19, ResNet50, ResNet152V2, DenseNet121, InceptionResNetV2, and MobileNetV2. The results show that the fine-tuned VGG19, DenseNet21 and InceptionNet models achieve compatible results with the literature. Moreover, the Xception model outperformed the compared TL models and literature methods in terms of recall, precision, accuracy, and F1 score. Moreover, it achieved on the testing dataset 73.7, 88, 80.2, and 94.75% of recall, precision, F1 score, and accuracy, respectively, and on the validation dataset 73.3, 90.24, 80.9, and 95% of recall, precision, F1 score, and accuracy, respectively.