发表机构
Jahangirnagar University（贾汉吉尔纳加尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于DenseNet-121的注意力引导三分支融合框架，在SSPD及三类病灶图像数据集上验证其性能，融合分支表现更优，可提升医疗图像分析的模型透明度与决策可靠性。
AI 中文摘要
病灶聚焦图像分类面临核心分析挑战，因为判别性信号通常较为稀疏、空间分散且易被背景噪声掩盖，而传统卷积神经网络（CNN）会对整幅图像进行均匀处理，可能削弱信号的相关性。本研究假设，自适应融合全局上下文信息与病灶聚焦的局部信息，相比单独使用任一表示，可提升分类性能。我们提出一种基于密集连接卷积网络-121（DenseNet-121）构建的三分支注意力引导深度学习框架，以改善特征归因、可解释性和分类可靠性。该架构包含从整幅图像学习表示的全局分支，随后通过梯度加权类激活映射（Grad-CAM）生成凸显与预测相关区域的注意力图并生成掩码输入，以及经卷积块注意力模块（CBAM）增强的局部分支，用于从这些聚焦区域提取精细的空间和通道特征。自适应融合分支通过学习实例特定的权重来整合全局和局部表示，允许在上下文信息与局部信息之间进行动态优先级分配。该框架在合成斑点模式数据集（SSPD）及三个基准数据集（包括皮肤病灶、番石榴叶片和葡萄叶片图像数据集）上进行评估，其中融合分支的性能优于单独的全局分支和局部分支，在皮肤病灶数据集上达到97.75%的准确率，在番石榴叶片数据集上达到99.64%的准确率。这些结果凸显了注意力引导架构在医疗分析中的价值，其可提升模型透明度、增强特征相关性，并为医学图像分析中更可靠的数据驱动决策提供支持。
英文摘要
Lesion-focused image classification presents a core analytical challenge, as discriminative signals are often sparse, spatially dispersed, and easily obscured by background noise, while conventional convolutional neural networks (CNNs) process entire images uniformly and may dilute signal relevance. This study hypothesizes that adaptive fusion of global contextual information and lesion-focused local information can improve classification performance compared with using either representation independently. We propose a three-branch, attention-guided deep learning framework built on Densely Connected Convolutional Network-121 (DenseNet-121) to improve feature attribution, interpretability, and classification reliability. The architecture consists of a global branch that learns representations from full images, followed by Gradient-weighted Class Activation Mapping (Grad-CAM) to generate attention maps that highlight prediction-relevant regions and produce masked inputs, and a local branch enhanced with a Convolutional Block Attention Module (CBAM) to extract refined spatial and channel-wise features from these focused regions. An adaptive fusion branch integrates global and local representations by learning instance-specific weights, allowing dynamic prioritization between contextual and localized information. The framework is evaluated on a synthetic Spot Pattern Dataset (SSPD) and three benchmark datasets, including skin lesion, guava leaf, and grape leaf image datasets, where the fusion branch outperformed the individual global and local branches, reaching 97.75% accuracy on the skin lesion dataset and 99.64% on the guava leaf dataset. The results highlight the value of attention-guided architectures in healthcare analytics by improving model transparency, strengthening feature relevance, and supporting more reliable data-driven decision-making in medical image analysis.
