Applications Of Artificial Intelligence In Image Processing Field Using Matlab Fixed: Artificial Neural Networks Applied For Digital Images With Matlab Code The

| Challenge | Solution in MATLAB | |-----------|--------------------| | Overfitting | Use imageDataAugmenter , dropout layers ( dropoutLayer ), L2 regularization ( trainingOptions ) | | Small datasets | Transfer learning ( squeezenet , alexnet ) or synthetic data generation | | Long training time | trainingOptions('ExecutionEnvironment','auto') with GPU; reduce mini-batch size | | Class imbalance | Use classWeights in classificationLayer or oversampling | | Interpretability | Use occlusionSensitivity , gradCAM , deepDreamImage functions |

The image processing field has been radically transformed by Artificial Intelligence (AI). Traditional algorithms (filters, edge detectors, morphology) are deterministic. In contrast, AI—specifically Artificial Neural Networks (ANNs)—learn patterns directly from data. MATLAB provides an integrated environment where you can design, train, and deploy ANNs for image tasks without switching between languages. MATLAB provides an integrated environment where you can