A next-generation hybrid generative learning framework integrating dual GAN and Swin Transformer–CNN for robust early diagnosis of pomegranate diseases

A Deep Hybrid CNN–Transformer Framework Combining EfficientNet and Vision Transformer for Multiclass Skin Cancer Classification

Skin cancer remains one of the most prevalent and life-threatening dermatological diseases worldwide. Early and precise detection plays a vital role in improving patient survival rates and reducing treatment costs. This paper presents a hybrid deep learning framework that integrates EfficientNetB0 and Vision Transformer (ViT) architectures to perform multiclass classification of dermoscopic skin lesions. The model is trained on the HAM10000 dataset, which includes eight types of skin cancer lesions, using transfer learning and data augmentation to improve generalization. EfficientNetB0 efficiently captures local spatial and texture features, while ViT models global contextual dependencies through self-attention mechanisms. Experimental evaluation demonstrates that the hybrid model achieves a validation accuracy of 82.73%, outperforming EfficientNetB0 (80.25%) and ViT (81.12%) by 2.48% and 1.61%, respectively. Additionally, the proposed framework achieves a macro precision of 0.7512, macro recall of 0.6158, and macro F1-score of 0.6505, confirming its superior classification capability. These results demonstrate that the hybrid Efficient Net–ViT architecture provides a robust, scalable, and reliable solution for automated skin cancer diagnosis and establishes a foundation for clinical AI applications.

Adaptive Feature Integration in CNN–Transformer Networks for Efficient and Interpretable Visual Classification

Over the past few years, deep learning has changed substantially following the emergence of Transformer architectures, which are particularly effective for representing long-range dependencies that are difficult for conventional Convolutional Neural Networks (CNNs). Whereas CNNs are well suited to extracting local spatial features using convolutional operations, Transformers are effective at representing global context through self-attention. Hybrid CNN–Transformer architectures have been developed to combine the respective strengths of the two approaches. A limitation of many existing models is their reliance on static or manually designed fusion strategies, which can restrict adaptability, add computational cost, and make the resulting decisions harder to interpret. The present study develops a novel adaptive fusion framework that adaptively combines CNN and Transformer features through learnable gating, attention-based feature integration, and explainable-AI methods. The resulting framework is intended to improve both computational efficiency and model interpretability, thereby addressing important limitations of current hybrid designs. The experimental evaluation uses benchmark datasets such as ImageNet, CIFAR-100, and medical imaging datasets. The reported results show that the proposed model performs better than the comparison architectures with respect to accuracy, efficiency, and interpretability.

Multi-Modal Medical Image Fusion Using Hybrid CNN-Transformer Models for Early Detection of Chronic Diseases

Chronic disease early and accurate detection is a major healthcare issue nowadays, and most importantly, there is the rising prevalence or use of heterogeneous medical image data such as CT, MRI, X-ray, and retinal scans. Conventional models of deep learning such as CNNs perform well on spatial aspects of feature extraction but not generally on long-term relations and overall context. In this paper, we introduce a new hybrid deep learning network combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to carry out multi-modal medical image fusion to assist in the diagnostic process with better results. The CNN branch gives fine-grain local representation and the Transformer module will predict the global connections between modalities. Empirical tests on publicly available data show that the proposed model is better than single CNN and Transformer models in using the datasets and there are massive increments in precise score, recall, and F1-score on chronic disease diagnosis in the initial stages. The field of research shows how hybrid architecture can be used to combine complementary information about various scanning modalities, which can become the direction of AI-aided decision-making in prevention.

GTUNet: a GNN and transformer enhanced U-Net for coronary artery segmentation

A Hybrid Deep Learning Architecture Integrating YOLOv8 and ResNet for Lung Disease Diagnosis

This project introduces an AI-powered diagnostic platform designed to identify major respiratory conditions from chest X-ray images using a advanced hybrid Deep Learning architecture. By integrating YOLOv8 for precise lesion localization and ResNet50 for deep feature extraction, the system overcomes the limitations of traditional single-model approaches, offering a more detailed analysis of lung pathology. The model is trained on a comprehensive dataset encompassing four critical categories: Normal, COVID-19, Pneumonia, and Tuberculosis. To ensure clinical reliability, the system employs advanced preprocessing including normalization and augmentation to handle variations in X-ray quality. This dual-network engine is integrated into a responsive web application that provides healthcare providers with near-instantaneous diagnostic results and confidence scores. With a user-friendly interface designed for both specialists and general practitioners, the platform bridges the gap in medical expertise, particularly in resource-limited or remote regions. By combining automated detection with accessible web technology, this research provides a scalable solution to accelerate clinical decision-making and improve patient outcomes in respiratory healthcare.

An Attention-Driven DenseNet–Vision Transformer (ViT) Architecture for Multi-label Chest X-ray Classification

Among the most challenging issues with respect to multilabel disease classification through the use of chest X-rays is the difficulty posed by the pattern of lesions that arise in the image besides the presence of class similarities and multiple thoracic diseases appearing in one image. As a result, abnormalities can be hard to see because of the image size in the overall size of the X-ray image and because of the complexity of the relationships between the different disease labels. Therefore, making a correct diagnosis of the diseases using current deep neural network models is very challenging. For these reasons, a new framework is introduced to model both local and global contextual information and also to identify the relationship between the different diseases by developing models. This paper proposes a new hybrid framework by combining both convolutional neural networks (CNNs) and vision transformers (ViT) to overcome the challenges that occur in terms of accurately diagnosing diseases in chest X-ray imaging. The proposed framework utilizes DenseNet121 for extracting local spatial features and ViT for capturing global contextual dependencies from chest X-ray images. Further, the proposed framework incorporates several methods for multi-scale feature learning and feature fusion from the different scales to detect lesions more effectively. Graph-based label dependency learning and contrastive feature learning are two effective approaches that are used to explore the discriminability of features and learn how labels correlate to one another. The hybrid model is tested on the NIH ChestX-ray14 dataset, which has 14 separate categories of diseases. Performance is measured using the evaluation metric Area Under the Curve (AUC). The final hybrid model demonstrates an average AUC score of 0.8303 compared to the baseline DenseNet121 model's AUC score of 0.797, indicating that this hybrid approach is effective in classifying diseases. Furthermore, the hybrid approach demonstrates higher accuracy and reliability than traditional methods.