InceptionMamba: A Lightweight and Accurate Model for Medical Image Classification Revealing Mamba’s Low-Frequency Bias

InceptionMamba: A Lightweight and Accurate Model for Medical Image Classification Revealing Mamba’s Low-Frequency Bias | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article InceptionMamba: A Lightweight and Accurate Model for Medical Image Classification Revealing Mamba’s Low-Frequency Bias BingQuan Huang, Yue Liu, Bin Tang, Gang Fang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7962407/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Neural Processing Letters → Version 1 posted 9 You are reading this latest preprint version Abstract In previous research on medical image classification, CNNs cannot capture long-range dependencies with local receptive fields and result in poor classification performance. Transformer-based models are limited by the quadratic computational complexity of the self-attention mechanism, especially when processing high-resolution medical images. It is difficult to deploy them in limited computational settings without sacrificing performance. Mamba-based models have attracted a lot of interests in computer vision due to their linear computation complexity. Despite their low FLOPs, Mamba-based models with less parameters perform sub-optimally in image classification tasks. To overcome the limitations of Mamba-based models, we propose InceptionMamba, a model that combines lightweight design with high accuracy for medical image classification tasks.Inspired by the impressive performance of the Inception architecture at a relatively low computational cost, we introduce Inception modules Mamba-based model. Meanwhile, a channel attention mechanism is employed to improve performance. Additionally, we conduct an in-depth analysis of the modeling capabilities of State Space Model (SSM) from the perspective of frequency response, revealing that it is better suited for medical images dominated by low-frequency components rather than natural images dominated by high-frequency information. InceptionMamba demonstrates competitive performance on medical image classification tasks, surpassing most state-of-the-art methods. The source code is publicly available at https://github.com/pepper1329/InceptionMamba. Medical Image Classification State Space models Inception Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Neural Processing Letters → Version 1 posted Editorial decision: Revision requested 20 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 27 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7962407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544547733,"identity":"0cceab1c-3d53-4f41-9502-4f8cd811bb33","order_by":0,"name":"BingQuan Huang","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"BingQuan","middleName":"","lastName":"Huang","suffix":""},{"id":544547735,"identity":"4e16e178-6e0b-40fa-8691-58b1f9ce9afe","order_by":1,"name":"Yue Liu","email":"","orcid":"","institution":"Guangzhou 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