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arXiv · 2509.03041

MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model

Pengyang Yu·Haoquan Wang·Gerard Marks·Tahar Kechadi·Laurence T. Yang·Sahraoui Dhelim·Nyothiri Aung
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Abstract

Accurate skin-lesion segmentation remains a key technical challenge for computer-aided diagnosis of skin cancer. Convolutional neural networks, while effective, are constrained by limited receptive fields and thus struggle to model long-range dependencies. Vision Transformers capture global context, yet their quadratic complexity and large parameter budgets hinder use on the small-sample medical datasets common in dermatology. We introduce the MedLiteNet, a lightweight CNN Transformer hybrid tailored for dermoscopic segmentation that achieves high precision through hierarchical feature extraction and multi-scale context aggregation. The encoder stacks depth-wise Mobile Inverted Bottleneck blocks to curb computation, inserts a bottleneck-level cross-scale token-mixing unit to exchange information between resolutions, and embeds a boundary-aware self-attention module to sharpen lesion contours.

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Pengyang Yu, Haoquan Wang, Gerard Marks, Tahar Kechadi, Laurence T. Yang, Sahraoui Dhelim, Nyothiri Aung. 2025-09-03. MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model. https://arxiv.org/abs/2509.03041

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