CNNs are the gold standard for image processing because they preserve spatial structure using convolutional and pooling layers. MATLAB simplifies transfer learning—retraining the last layers of a proven architecture like AlexNet, GoogLeNet, or ResNet.
% Flatten into a row vector for neural network input feature_vector = img_resized(:)'; % 1x4096 vector CNNs are the gold standard for image processing
| 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 | For raw pixel inputs, performance would be poor
This simple MLP achieves classification by learning nonlinear boundaries in feature space. For raw pixel inputs, performance would be poor due to lack of spatial invariance—hence the need for CNNs. For raw pixel inputs