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Intraclass heterogeneity of medical diagnostic objects poses a challenge for accurate intraclass classification of medical fine-grained images (MFGIs) within deep learning. To accurately classify MFGIs, we propose a novel approach termed fuzzy channel filter-based separable convolution neural networks (FSCNN). The original design of FSCNN comprises the following components: 1) Designing the fuzzy channel filter (FCF) module, devised to establish long-distance feature dependencies for each feature channel with the input image by formulating fuzzy rules “IF–THEN”. 2) The FCF-based separable convolution (FSC) block uses depth-wise and point-wise convolutions to extract and mix feature channels. Then, the internal information of each feature channel is reintegrated through fuzzy weighted averaging in FCF to enhance fine-grained feature information. 3) Creating the deep fuzzy learning architecture FSCNN through the superimposition of FSC blocks. This architectural arrangement enables more effective learning of fine-grained feature distinctions within MFGIs, thereby enhancing classification accuracy. Compared to other advanced fine-grained classification models, including state-of-the-art models, our model outperforms by 2%–6% and 3%–9% on brain MRI and pneumonia CT datasets, respectively.