Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain’s blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model’s performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings.

Background: While online food delivery applications (OFDAs) offer convenient food accessibility, their impact on dietary behaviors remains insufficiently explored, especially in the Arab region. This study applies machine learning (ML) techniques to identify the key behavioral and nutritional factors contributing to dietary disruption linked to OFD platforms.Methods: We conducted a cross-sectional study which involved 7,370 adults across 10 Arab countries using a comprehensive online survey. The study employed an ensemble ML approach, comparing Random Forest, XGBoost, CatBoost, and LightGBM tree-based models to analyze 31 features across six domains: demographics, ordering frequency, food preferences, nutritional perceptions, behavioral factors, and service attributes. Model performance was evaluated using multiple metrics, including sensitivity, precision, F1-score, and AUC. Clear interpretation of the risk factors was explained using partial dependence plots.Results: The findings revealed that the strongest predictors of dietary disruption were excessive food consumption, altered meal routines, and preferences for fatty foods. Younger individuals, males, and those with higher BMI reported higher disruption rates. Lebanon and Bahrain showed the highest rates for notable disruption, while Oman reported the lowest. ML analysis demonstrated high predictive performance, with Random Forest achieving the highest sensitivity (94.3%) and F1-score (89.3%). Feature importance analysis identified behavioral factors as more influential than socioeconomic indicators.Conclusion: OFDAs offer valuable convenience and market expansion while simultaneously posing significant challenges to maintaining optimal dietary health. With strategic interventions and public health collaborations, these platforms can shift from being disruptors of healthy dietary habits to catalysts for improved nutrition and well-being in the Arab region and beyond.