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This paper introduces a hybrid deep learning model for medical image diagnostics that combines CNNs for spatial feature extraction, RNNs for temporal pattern detection, and GANs for data augmentation and anomaly detection. The proposed Transformer-guided architecture aims to improve diagnostic accuracy and processing speed while addressing challenges related to noisy data and insufficient annotations. Experimental results demonstrate that the integrated framework achieves superior diagnostic performance, outperforming ten baseline models with 90% accuracy, 88% precision, 86% sensitivity, and 0.95 ROC-AUC.
A hybrid CNN-RNN-GAN architecture guided by Transformers achieves state-of-the-art accuracy in medical image diagnostics while maintaining computational efficiency.
Introduction: Medical imaging serves as a crucial tool for disease diagnosis but current image analysis techniques fail to handle noisy data and insufficient annotations and different imaging modalities. Deep learning techniques have transformed medical imaging but achieving high diagnostic accuracy alongside computational efficiency remains a key challenge in clinical deployment. Objective: The research proposes a single deep learning system which combines CNNs with RNNs and GANs to enhance automated disease detection from medical images through improved accuracy, better interpretability and faster processing times.Method: The proposed Transformer-guided hybrid model uses CNNs to extract spatial features and RNNs to detect temporal patterns while GANs perform data augmentation and anomaly detection. Use consistent passive or active voice. The model was trained, validated on multimodal datasets and subsequently evaluated against ten baseline models, including SVM, transfer learning, and attention-based architectures. The evaluation metrics consisted of accuracy and precision and sensitivity and ROC-AUC.Results: The integrated framework achieved superior diagnostic performance with 90% accuracy, 88% precision, 86% sensitivity and 0.95 ROC-AUC which outperformed all baseline models. The system delivered achieved faster processing without sacrificing diagnostic accuracy across imaging modalities without compromising its diagnostic accuracy for different imaging techniques. Conclusions: The research developed an AI diagnostic system which uses CNN, RNN and GAN technologies to achieve efficient and ethical medical image analysis. The system enhances precision and speed while ensuring patient data security and transparent clinical reporting, enabling scalable AI-driven diagnostics.
