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Nowadays, images are fundamental data source in modern medicine. The images stored in a database according with categories are an important step for data mining and content-based image retrieval (CBIR). These can support doctors and students in diagnostic decisions and provide research and didactic material. This work addresses the use of discrete wavelet transform and self-organizing map (SOM) to medical image categorization. Furthermore, extensive experiments to define map size, finetune using linear vector quantization and a contrastive study with another success approach of categorization are realized.