基于轻量级卷积神经网络的移动Android终端胆石症人工智能诊断系统数据集

数据集获取（Figshare）

• 源站标题：An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network
• DOI：10.1371/journal.pone.0221720
• 发布日期：2019-09-12
• 文件数：6 个
• 文件总体积：82.12 MB
• 源站页面：https://plos.figshare.com/articles/dataset/An_artificial_intelligent_diagnostic_system_on_mobile_Android_terminals_for_cholelithiasis_by_lightweight_convolutional_neural_network/9818843

命令行下载

curl -L -o "pone.0221720.s001.tif" "https://ndownloader.figshare.com/files/17612819"
curl -L -o "pone.0221720.s002.tif" "https://ndownloader.figshare.com/files/17612822"
curl -L -o "pone.0221720.s003.tif" "https://ndownloader.figshare.com/files/17612825"
curl -L -o "pone.0221720.s004.tif" "https://ndownloader.figshare.com/files/17612828"
curl -L -o "pone.0221720.s005.tif" "https://ndownloader.figshare.com/files/17612831"
curl -L -o "pone.0221720.s006.tif" "https://ndownloader.figshare.com/files/17612834"

文件清单

• pone.0221720.s001.tif（13.69 MB）
• pone.0221720.s002.tif（13.69 MB）
• pone.0221720.s003.tif（13.69 MB）
• pone.0221720.s004.tif（13.69 MB）
• pone.0221720.s005.tif（13.69 MB）
• pone.0221720.s006.tif（13.69 MB）

文件总体积 82.12 MB（源站 API 实测）。

数据说明

Artificial intelligence (AI) tools have been applied to diagnose or predict disease risk from medical images with recent data disclosure actions, but few of them are designed for mobile terminals due to the limited computational power and storage capacity of mobile devices. In this work, a novel AI diagnostic system is proposed for cholelithiasis recognition on mobile devices with Android platform. To this aim, a data set of CT images of cholelithiasis is firstly collected from The Third Hospital of Shandong Province, China, and then we technically use histogram equalization to preprocess these CT images. As results, a lightweight convolutional neural network is obtained in a constructive way to extract cholelith features and recognize gallstones. In terms of implementation, we compile Java and C++ to adapt to the application of deep learning algorithm on mobile devices with Android platform. Noted that, the training task is completed offline on PC, but cholelithiasis recognition tasks are performed on mobile terminals. We evaluate and compare the performance of our MobileNetV2 with MobileNetV1, Single Shot Detector (SSD), YOLOv2 and original SSD (with VGG-16) as feature extractors for object detection. It is achieved that our MobileNetV2 achieve similar accuracy rate, about 91% with the other four methods, but the number of parameters used is reduced from 36.1M (SSD 300, SSD512), 50.7M (Yolov2) and 5.1M (MobileNetV1) to 4.3M (MobileNetV2). The complete process on testing mobile devices, including Virtual machine, Xiaomi 7 and Htc One M8 can be controlled within 4 seconds in recognizing cholelithiasis as well as the degree of the disease.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0221720（source: https://plos.figshare.com/articles/dataset/An_artificial_intelligent_diagnostic_system_on_mobile_Android_terminals_for_cholelithiasis_by_lightweight_convolutional_neural_network/9818843）。