A Hybrid CNN-MLP-DWD Framework for Robust Medical Image Classification Under High-Dimensional Low-Sample Size Conditions

Medical image classification in clinical settings is frequently constrained by High-Dimensional, Low-Sample Size (HDLSS) conditions, rendering conventional Support Vector Machines (SVM) geometrically susceptible to the data piling phenomenon. This study proposes a hybrid CNN-DWD framework to address this geometrical instability by integrating multi-architecture convolutional feature extraction with Distance-Weighted Discrimination (DWD). Pre-trained ResNet50 and DenseNet121 backbones act as frozen feature extractors, generating a highly descriptive 3072-dimensional fused representation. To resolve the computational bottleneck of deploying DWD directly on massive feature spaces, a supervised Multi-Layer Perceptron (MLP) bottleneck progressively compresses this space into a 32-dimensional latent manifold. Evaluated across breast ultrasonography, breast mammography, and chest X-ray datasets under varying training allocations, the proposed architecture drastically accelerates DWD training—achieving over a 130-fold speedup. The proposed CNN-MLP-DWD framework demonstrates highly competitive diagnostic performance, achieving 93.16% accuracy on the breast ultrasonography dataset and a macro-AUC of 99.69% on the chest X-ray benchmark, comparing favorably against the evaluated baseline methods.