Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.

Lung cancer is the most common cause of cancer-related mortality globally. Early diagnosis of this highly fatal and prevalent disease can significantly improve survival rates and prevent its progression. Computed tomography (CT) is the gold standard imaging modality for lung cancer diagnosis, offering critical insights into the assessment of lung nodules. We present a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs). By optimizing and integrating grid and block attention mechanisms with InceptionNeXt blocks, the proposed model effectively captures both fine-grained and large-scale features in CT images. This comprehensive approach enables the model not only to differentiate between malignant and benign nodules but also to identify specific cancer subtypes such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. Similarly, including grid attention improves the model’s capacity to identify spatial relationships across different sections of the picture, whereas block attention focuses on capturing hierarchical and contextual information, allowing for precise identification and categorization of lung nodules. To ensure robustness and generalizability, the model was trained and validated using two public datasets, Chest CT and IQ-OTH/NCCD, employing transfer learning and pre-processing techniques to improve detection accuracy. The proposed model achieved an impressive accuracy of 99.54% on the IQ-OTH/NCCD dataset and 98.41% on the Chest CT dataset, outperforming state-of-the-art CNN-based and ViT-based methods. With only 18.1 million parameters, the model provides a lightweight yet powerful solution for early lung cancer detection, potentially improving clinical outcomes and increasing patient survival rates.

This study focused on to examine prevalence of nomophobia among university students; and the relationship among nomophobia, self-esteem, loneliness and self-happiness with respect to gender and year of study of the university students in Pakistan and Turkey. The study subjects consisted of 729...

Agriculture forms the cornerstone of global food security, with olives playing a pivotal role not only as a food source but also in cosmetics, medicine, and other industries. However, diseases affecting olive trees pose significant threats to agricultural productivity and economic stability, underscoring the need for innovative detection solutions. A promising solution to these challenges is the development of deep learning-based computer-aided diagnostic applications, which have shown remarkable success in various fields, especially in recent years. This study presents a novel deep-learning approach for olive leaf disease detection, introducing a MetaFormer-based architecture that combines the power of transformer-based components, specifically separable self-attention, with the efficiency of a lightweight design. The proposed model was evaluated using two distinct datasets, Dataset-1 and Dataset-2, where it achieved impressive accuracy rates of 99.31% and 96.91%, respectively. When compared to other cutting-edge models such as Swin-Base, MaxViT-Base, DeiT3-Base, CAFormer-s18, CAFormer-m36, ResNet50, and MobileNetv3, the Proposed Model outperformed them in terms of accuracy, precision, recall, and F1-score. These advancements were made possible through the incorporation of separable self-attention, which allows for capturing both local and global dependencies in olive leaf images, and a streamlined architecture that reduces computational complexity without sacrificing performance. Furthermore, Grad-CAM visualizations highlighted the interpretability of the model, confirming its ability to focus on disease-relevant regions of the images. This study offers a significant contribution to the field of agricultural disease detection, particularly in olive farming, and sets the stage for future work in adapting the model for other crops and real-time applications in agriculture.