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<p>Deep learning has significantly improveded medical image classification through Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, CNNs struggle to capture long-range dependencies, while ViTs require substantial computational demands. To address these limitations, we introduce an <b>A</b>daptive <b>L</b>ightweight sequential space model, named <i>ALMamba</i>. This novel architecture integrates Structured State Space Model (SSMs) with an Adaptive Channel Modulation and Enhancement Block, within a unified SS-ACME-SSM block. By using channel splitting and separate global and local feature processing, ALMamba effectively captures both detailed and broad image features. To prevent information loss, we implement a channel shuffle operation followed by channel concatenation. The proposed ALMamba framework significantly reduces the model parameters and computational complexity, making it suitable for efficient medical imaging tasks. Extensive experiments on six diverse medical image datasets demonstrate, ALMamba competitive performance, achieving 1.93G Flops and 8.60M parameters, surpassing several state-of-the-art methods in medical image classification. This study shows a new benchmark and offers valuable insights for advancing SSM-based AI algorithms in the healthcare domain.</p>

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An Adaptive Lightweight Sequence Space Model for Medical Image Classification

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Tulasiram Jinaga,
  • Balaji Banothu,
  • S. Nickolas,
  • Gourav Rajgonda Patil

摘要

Deep learning has significantly improveded medical image classification through Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, CNNs struggle to capture long-range dependencies, while ViTs require substantial computational demands. To address these limitations, we introduce an Adaptive Lightweight sequential space model, named ALMamba. This novel architecture integrates Structured State Space Model (SSMs) with an Adaptive Channel Modulation and Enhancement Block, within a unified SS-ACME-SSM block. By using channel splitting and separate global and local feature processing, ALMamba effectively captures both detailed and broad image features. To prevent information loss, we implement a channel shuffle operation followed by channel concatenation. The proposed ALMamba framework significantly reduces the model parameters and computational complexity, making it suitable for efficient medical imaging tasks. Extensive experiments on six diverse medical image datasets demonstrate, ALMamba competitive performance, achieving 1.93G Flops and 8.60M parameters, surpassing several state-of-the-art methods in medical image classification. This study shows a new benchmark and offers valuable insights for advancing SSM-based AI algorithms in the healthcare domain.