Our research focuses on the critical field of early diagnosis of disease by
examining retinal blood vessels in fundus images. While automatic segmentation
of retinal blood vessels holds promise for early detection, accurate analysis
remains challenging due to the limitations of existing methods, which often
lack discrimination power and are susceptible to influences from pathological
regions. Our research in fundus image analysis advances deep learning-based
classification using eight pre-trained CNN models. To enhance interpretability,
we utilize Explainable AI techniques such as Grad-CAM, Grad-CAM++, Score-CAM,
Faster Score-CAM, and Layer CAM. These techniques illuminate the
decision-making processes of the models, fostering transparency and trust in
their predictions. Expanding our exploration, we investigate ten models,
including TransUNet with ResNet backbones, Attention U-Net with DenseNet and
ResNet backbones, and Swin-UNET. Incorporating diverse architectures such as
ResNet50V2, ResNet101V2, ResNet152V2, and DenseNet121 among others, this
comprehensive study deepens our insights into attention mechanisms for enhanced
fundus image analysis. Among the evaluated models for fundus image
classification, ResNet101 emerged with the highest accuracy, achieving an
impressive 94.17%. On the other end of the spectrum, EfficientNetB0 exhibited
the lowest accuracy among the models, achieving a score of 88.33%. Furthermore,
in the domain of fundus image segmentation, Swin-Unet demonstrated a Mean Pixel
Accuracy of 86.19%, showcasing its effectiveness in accurately delineating
regions of interest within fundus images. Conversely, Attention U-Net with
DenseNet201 backbone exhibited the lowest Mean Pixel Accuracy among the
evaluated models, achieving a score of 75.87%.

我们的研究聚焦于通过检查眼底图像中的视网膜血管进行疾病的早期诊断。我们利用八个预训练的 CNN 模型进行基于深度学习的分类，同时应用了可解释的 AI 技术，如 Grad-CAM、Grad-CAM++、Score-CAM、Faster Score-CAM 和 Layer CAM。通过对多个模型进行评估，我们发现 ResNet101 具有最高的准确率（94.17%），而 EfficientNetB0 的准确率最低（88.33%）。对于图像分割，Swin-Unet 表现出 86.19% 的平均像素准确率，而 Attention U-Net 基于 DenseNet201 的准确率最低（75.87%）。

基于可解释卷积神经网络的视网膜底部分类及基于先进视网膜血管图像分割模型

Explainable Convolutional Neural Networks for Retinal Fundus  Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels  from Fundus Images

The caliber and configuration of retinal blood vessels serve as important
biomarkers for various diseases and medical conditions. A thorough analysis of
the retinal vasculature requires the segmentation of blood vessels and their
classification into arteries and veins, which is typically performed on color
fundus images obtained by retinography, a widely used imaging technique.
Nonetheless, manually performing these tasks is labor-intensive and prone to
human error. Various automated methods have been proposed to address this
problem. However, the current state of art in artery/vein segmentation and
classification faces challenges due to manifest classification errors that
affect the topological consistency of segmentation maps. This study presents an
innovative end-to-end framework, RRWNet, designed to recursively refine
semantic segmentation maps and correct manifest classification errors. The
framework consists of a fully convolutional neural network with a Base
subnetwork that generates base segmentation maps from input images, and a
Recursive Refinement subnetwork that iteratively and recursively improves these
maps. Evaluation on public datasets demonstrates the state-of-the-art
performance of the proposed method, yielding more topologically consistent
segmentation maps with fewer manifest classification errors than existing
approaches. In addition, the Recursive Refinement module proves effective in
post-processing segmentation maps from other methods, automatically correcting
classification errors and improving topological consistency. The model code,
weights, and predictions are publicly available at
this https URL

通过 RRWNet 模型，对视网膜血管进行分割和分类，有效地纠正错误分类，提高拓扑一致性。

RRWNet: 递归细化网络用于有效的视网膜动脉 / 静脉分割和分类

RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein  Segmentation and Classification

Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmentation task as a multi-label inference task and utilize the implicit
advantages of the combination of convolutional neural networks and structured
prediction. Our proposed convolutional neural network based model achieves
strong performance and significantly outperforms the state-of-the-art for
automatic retinal blood vessel segmentation on DRIVE dataset with 95.33%
accuracy and 0.974 AUC score.

本文提出了一种基于卷积神经网络的结构化预测模型，有效地解决了（DRIVE 数据集上）自动分割视网膜血管的任务，得到 95.33% 的准确率和 0.974 的 AUC 值。