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.

Safety of drivers and passengers is an important quality assurance aim of the tire manufacturers. Tires that have air bubbles in them are a frequent cause of accidents. Recently, manufacturers have placed a high value on employing laborers with tire diagnostic experience. Along with the expenditure of time and money, this process also necessitates a sizable workforce of experienced laborers. The detection of tire defects can be automated utilizing a number of different ways, greatly reducing the margin for human error. One such method is digital shearography, which can be used to detect air bubbles in images. In this study, transfer learning based models are proposed to classify air bubble defects in tire shearography images. Proposed method employed in transfer learning with several deep learning CNN-based models, including VGG16, VGG19, ResNet50, Xception, MobileNetV2 and DenseNet201. Owing to the assistance of the experts at the Pirelli Automobile Tires Izmit factory, we were able to collect and label the dataset used in this study. The dataset contains 1392 tire shearography images. 811 of these images belong to the ‘good’ class, while the remaining 581 images belong to the ‘bad’ class. The proposed method in this study achieves remarkable bubble detection results over 95% in terms of accuracy and other metrics with the DenseNet201 model. The results indicate that the proposed model has comparable accuracy and recall with the existing studies. This study is valuable for the tire industry, as the existing literature offers limited research on the classification of tire bubble defects, the differentiation between defective and defect-free tires, and the categorization of various defect types. In addition, the proposed model offers several advantages over existing studies, including higher robustness against overfitting, suitability for real-time industrial applications, and improved efficiency in processing, making a practical solution for defect detection in industrial settings.

COVID-19 prediction plays a crucial role in medical decision-making for respiratory health. Accurate and rapid prediction using advanced artificial intelligence (AI) techniques is particularly vital during pandemics. Recent research in the medical field has extensively explored and applied AI-based technologies such as deep learning, attention mechanisms, vision transformers, and explainable AI to various diseases, including COVID-19. However, the limited diversity of medical data poses challenges in accurately evaluating AI models' generalization capabilities for prediction. To address these limitations, we have gathered a comprehensive collection of diverse benchmarks on respiratory diseases, including COVID-19, from various sources. This dataset allows us to assess and compare the performance of AI models under consistent training and testing environments. Our findings aim to inspire researchers in the field and offer valuable insights into the future use of AI techniques in the medical domain. The best classification performance is achieved using ResNet152V2 recording overall accuracy of 96.17%.

Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view data and poor generalization to new viewpoints. We introduce BenchSeg, a novel multi-view food video segmentation dataset and benchmark. BenchSeg aggregates 55 dish scenes (from Nutrition5k, Vegetables & Fruits, MetaFood3D, and FoodKit) with 25,284 meticulously annotated frames, capturing each dish under free 360° camera motion. We evaluate a diverse set of 20 state-of-the-art segmentation models (e.g., SAM-based, transformer, CNN, and large multimodal) on the existing FoodSeg103 dataset and evaluate them (alone and combined with video-memory modules) on BenchSeg. Quantitative and qualitative results demonstrate that while standard image segmenters degrade sharply under novel viewpoints, memory-augmented methods maintain temporal consistency across frames. Our best model, based on a combination of SeTR-MLA+XMem2, outperforms prior work (e.g., improving over FoodMem by ~2.63% mAP), offering new insights into food segmentation and tracking for dietary analysis. In addition to frame-wise spatial accuracy, we introduce a dedicated temporal evaluation protocol that explicitly quantifies segmentation stability over time through continuity, flicker rate, and IoU drift metrics. This allows us to reveal failure modes that remain invisible under standard per-frame evaluations. We release BenchSeg to foster future research. The project page, including the dataset annotations and the food segmentation models, can be found at https://amughrabi.github.io/benchseg.

The safety and reliability of automobile tires depend directly on the early detection of internal defects, since undetected air bubbles between rubber layers can propagate into catastrophic in-service failures. Although digital shearography has become the preferred non-destructive testing technique for revealing such subsurface anomalies, its industrial interpretation still relies on manual expert assessment, which is slow, operator-dependent, and difficult to audit. This study proposes an explainable deep learning framework for automated bubble detection in tire shearography images. The framework couples a DenseNet201 backbone with two complementary attention modules: a Spatial Aggregation Pyramid (SAP) that refines multi-scale feature representations through dilated depth-wise separable convolutions, and a Multi-Head Attention (MHA) module that models long-range contextual dependencies, so that both fine-grained texture variations and global spatial cues are captured. The model was developed on a class-balanced set of 1162 shearography images (581 per class) from 174 tire treads at the Pirelli İzmit factory, obtained by randomly excluding part of the non-defective images. Under 5-fold cross-validation, the proposed DenseNet201 SAP MHA model reached a mean accuracy of 0.962 with a standard deviation of 0.006 and an AUC (area under the curve) of 0.9794, consistently outperforming the DenseNet201 and DenseNet201 SAP baselines across accuracy, precision, recall, and F1-score. The improvements were consistent across all five folds and were supported by statistical testing. To address the black-box concern inherent in safety-critical inspection, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) were integrated, demonstrating that the model bases its decisions on diagnostically relevant regions rather than background artefacts. The framework therefore delivers not only high bubble detection accuracy but also a transparent and auditable diagnostic tool suitable for industrial quality control.