平均显著性图结果

数据集获取（Figshare）

• 源站标题：Average Saliency Map Results.
• DOI：10.1371/journal.pone.0332572.t007
• 发布日期：2025-09-29
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Average_Saliency_Map_Results_/30237923

命令行下载

curl -L -o "Table 7.xls" "https://ndownloader.figshare.com/files/58367123"

文件清单

• Table 7.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Alzheimer’s disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various types of artificial intelligence techniques, such as enhanced models of deep learning, for classifying Alzheimer’s disease. Therefore, this study proposes an Outline of deep learning to classify Alzheimer’s disease with segmentation using the Multi-Layer U-Net and a hybrid classification approach combining multi-scale EfficientNet with SVM. The proposed methodology consists of a four-phase process: (1) Whole brain segmentation, (2) Gray matter segmentation using multi-layer U-Net segmentation, (3) Feature extraction using Multi-Scale Efficient Net with SVM for classification, and (4) XAI (explainable AI) techniques by integrating Saliency Map Quantitative Analysis for increased clinical trustworthiness and model interpretability. It is found that the experiment results provide promising classification performance for three classes – Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) with an overall accuracy of 97.78% ± 0.54%, precision of 97.18% ± 1.14% (AD), 97.78% ± 0.29% (CN) and 97.03% ± 1.10% (MCI), recall of 97.90% ± 0.77% (AD), 97.49% ± 1.34% (CN) and 97.25% ± 0.99% (MCI), and F1 score of 97.74% ± 0.63% (AD), 97.78% ± 0.79% (CN), and 97.54% ± 0.69%(MCI). The results obtained underscore the elegance of the proposed approach in correctly classifying Alzheimer’s disease stages. Future work will evaluate the model on publicly accessible Alzheimer’s disease MRI datasets and incorporate advanced XAI techniques for increased interpretability and diagnostic reliability. The work focuses on Human Health.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0332572.t007（source: https://plos.figshare.com/articles/dataset/Average_Saliency_Map_Results_/30237923）。