ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
Source abstract
Abstract
Researchers encounter substantial challenges in medical image classification, mainly due to limited image resolution and low signal-to-noise ratios. This situation makes it difficult for deep learning algorithms to identify abnormal regions based solely on image content accurately. This paper proposes ResGDANet (Residual Group Dual-Channel Attention Network), an enhanced architecture that builds upon ResGANet by incorporating two novel modules: a Dual-Channel Attention Fusion (DCAF) module and a Retention-Memory Transformer (RMT) module. The DCAF module utilizes a dual-path architecture that integrates global average pooling and max pooling operations, effectively enhancing local feature representation through the fusion of channel-wise and spatial attention mechanisms. The RMT module enhances rotation-invariant feature extraction by integrating the retention mechanism from Retentive Networks and the global context modeling capabilities of Vision Transformers. Extensive experiments on the COVID19-CT and ISIC2018 datasets demonstrate the superiority of ResGDANet, achieving classification accuracies of 83.74% and 81.73% respectively, outperforming state-of-the-art models including ResGANet, GvT, and SENet. Ablation studies and visualization analyses further validate the efficacy of the proposed attention module, showing notable enhancements in feature representation capability and classification accuracy. By introducing a more robust and precise classification framework, this research contributes importantly to the progress in medical image analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · COVID-19 diagnosis using AI · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
References (41)
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