The categorization of medical images is essential for disease screening, diagnostic processes, and the design of therapeutic interventions. Existing methods mainly extract image features through a single spatial domain approach. However, medical images are characterized by significant noise interference, small interclass differences, and large intraclass differences, which result in relatively low classification accuracy. Therefore, this work designs a dual-path feature extraction mechanism that synergistically complements frequency and spatial domains as well as local and global features. It combines the strength of convolutional neural networks in extracting local features, the capacity of Transformers for global modeling, and the multi-scale analysis of discrete wavelet transform (DWT) to extract rich medical image features and improve classification accuracy. The model is mainly implemented through a dual-branch architecture. One branch uses a lightweight VGGNet to efficiently extract hierarchical local features and capture subtle pathological manifestations. The other branch combines DWT with the vision Transformer (ViT). First, DWT is used to perform multi-scale frequency domain decomposition on the image, where low frequencies retain organ morphology and lesion contours and high frequencies capture tissue microstructure and lesion boundaries, enhancing feature expression while suppressing noise. Then the ViT is used to establish cross-regional global semantic associations and model long-term dependencies. The features from the two branches are integrated through an adaptive fusion mechanism to form a comprehensive and robust representation. This method achieves an accuracy of 0.93 and an AUC of 0.94 on the public dataset Kaggle-EDC, with both sensitivity and specificity outperforming current mainstream models. Experimental results demonstrate that this framework markedly enhances both the classification performance and robustness on medical images, providing an effective solution for precise medical image analysis.