Meihua Zhou , Tianlong Zheng , Zhihua Wu , Nan Wan , Min Cheng
{"title":"DAMNet:阿尔茨海默病动态移动架构。","authors":"Meihua Zhou , Tianlong Zheng , Zhihua Wu , Nan Wan , Min Cheng","doi":"10.1016/j.compbiomed.2024.109517","DOIUrl":null,"url":null,"abstract":"<div><div>Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3 % in validation and 99.9 % in testing phases. Despite a 20 % pruning rate, DAMNet maintains consistent performance with less than 0.2 % loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7 % F1 score within 805 s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109517"},"PeriodicalIF":7.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DAMNet: Dynamic mobile architectures for Alzheimer's disease\",\"authors\":\"Meihua Zhou , Tianlong Zheng , Zhihua Wu , Nan Wan , Min Cheng\",\"doi\":\"10.1016/j.compbiomed.2024.109517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3 % in validation and 99.9 % in testing phases. Despite a 20 % pruning rate, DAMNet maintains consistent performance with less than 0.2 % loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7 % F1 score within 805 s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data.</div></div>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":\"185 \",\"pages\":\"Article 109517\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010482524016020\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524016020","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
DAMNet: Dynamic mobile architectures for Alzheimer's disease
Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3 % in validation and 99.9 % in testing phases. Despite a 20 % pruning rate, DAMNet maintains consistent performance with less than 0.2 % loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7 % F1 score within 805 s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.