Cognitive Imaging Intelligence: A Unified AI Framework for Image and Video Processing, Computer Vision, Pattern Recognition, and Medical Image Understanding

Artificial intelligence applications in imaging remain fragmented across image processing, video analysis, computer vision, pattern recognition, and medical image understanding. This fragmentation limits knowledge transfer, cross-domain generalization, and consistent explainability. This research introduces Cognitive Imaging Intelligence (CII) as a unified AI framework for heterogeneous visual intelligence.

The proposed framework integrates convolutional feature extraction, transformer-based attention, hierarchical representation learning, temporal attention, multimodal feature fusion, domain-adaptive learning, and explainable artificial intelligence. Evaluation employed CIFAR-10 for image classification, UCF101 for video action recognition, and ISIC 2018 for medical lesion segmentation and classification. The framework was compared with CNN, ResNet, a Vision Transformer, LSTM, CNN-LSTM, U-Net, and task-specific baseline architectures. Performance was measured using accuracy, precision, recall, F1-score, ROC-AUC, mean average precision, IoU, the Dice coefficient, inference latency, model complexity, and cross-domain generalization.

CII achieved 96.84% accuracy and a 96.71% F1-score on image classification, 94.63% recognition accuracy on UCF101, 91.42% Dice and 84.76% IoU for medical-image segmentation, and 96.18% AUC for medical-image classification. The framework improved performance over the strongest baseline by 3.27–7.84 percentage points across the evaluated tasks. Ablation analysis showed that shared representation learning, temporal attention, and domain adaptation contributed 2.91%, 2.36%, and 2.74%, respectively. Explainability evaluation achieved 93.58% localization consistency.

The findings demonstrate that CII provides a quantitatively validated and transferable foundation for unified image, video, pattern-recognition, and medical-image intelligence while improving predictive performance, interpretability, and cross-domain scalability.