Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

BMC Bioinformatics

Tian-Hsiang Huang¹  Fu-I Chou²  Hong-Siang Huang²  Jinn-Tsong Tsai³  Jyh-Horng Chou⁴  Wen-Hsien Ho⁵  Po-Yuan Yang⁶  Li-Chung Chi⁷ 

[1] Center for Big Data Research, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Chien-Kung Road, 807, Kaohsiung, Taiwan;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Computer Science, National Pingtung University, No. 4-18, Min-Sheng Road, 900, Pingtung, Taiwan;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Mechanical Engineering, National Chung-Hsing University, No. 145, Xingda Road, 402, Taichung, Taiwan;Department of Electrical Engineering, National Kaohsiung University of Science and Technology, No. 415, Chien-Kung Road, 807, Kaohsiung, Taiwan;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Medical Research, Kaohsiung Medical University Hospital, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Road, 407, Taichung, Taiwan;Department of Ophthalmology, Kaohsiung Medical University Hospital, No. 100, Shin-Chuan 1st Road, 807, Kaohsiung, Taiwan;Department of Ophthalmology, Kaohsiung Municipal SiaoGang Hospital, No. 482, Shanming Road, 812, Kaohsiung, Taiwan;

关键词: Residual Neural Network;    Uniform experimental design;    Hyperparameter optimization;    Macular degeneration classification;   

DOI  :  10.1186/s12859-021-04085-9

来源: Springer

【 摘 要 】

BackgroundThe prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness.ResultsAn open dataset of macular degeneration images (https://data.mendeley.com/datasets/rscbjbr9sj/3) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance.ConclusionThe high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.

【 授权许可】

CC BY