Optimizing Image Recognition Model through Transfer Learning and Robust Feature Extraction in Neural Networks

These advances in image identification's technology have created massive changes, mainly in the health care facilities plus self-directed systems. Transfer learning strategies coupled with robust feature extraction techniques are used in the model to promote efficiency in the imaging system. A technique of fine-tuning the weights used for recognition tasks on pre-trained convolutional neural network architectures is proposed. State of art feature extraction pipeline composed with edge detection, texture analysis and spatial frequency analysis to enrich network capacity of distinguishing intricate visual textures and patterns. This combination of techniques helps in faster model training with top class prediction results on mixed image datasets. After rigorous testing on various benchmark datasets, including CIFAR-10 and ImageNet, considerable enhancement in recognition performance beyond baselines was observed. Additionally, the proposed approach is resilient to changes in image quality, illumination, and occlusions. The proposed framework enhances the existing model efficiency, flexibility, and robustness for application in real-time image identification. This approach opens new capabilities for the medical imagery, self-navigation cars, and object recognize mechanisms whose image classification must be exact.