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Attentive Octave Convolutional Capsule Network for Medical Image Classification

التفاصيل البيبلوغرافية
العنوان: Attentive Octave Convolutional Capsule Network for Medical Image Classification
المؤلفون: Hong Zhang, Zhengzhen Li, Hao Zhao, Zan Li, Yanping Zhang
المصدر: Applied Sciences, Vol 12, Iss 5, p 2634 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: medical image classification, capsule network, octave convolution, attention mechanism, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Medical image classification plays an essential role in disease diagnosis and clinical treatment. More and more research efforts have been dedicated to the design of effective methods for medical image classification. As an effective framework, the capsule network (CapsNet) can realize translation equivariance. Lots of current research applies capsule networks in medical image analysis. In this paper, we propose an attentive octave convolutional capsule network (AOC-Caps) for medical image classification. In AOC-Caps, an AOC module is used to replace the traditional convolution operation. The purpose of the AOC module is to process and fuse the high- and low-frequency information in the input image simultaneously, and weigh the important parts automatically. Following the AOC module, a matrix capsule is used and the expectation maximization (EM) algorithm is applied to update the routing weights. The proposed AOC-Caps and comparative methods are tested on seven datasets, including PathMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, OrganMNIST_Axial, OrganMNIST_Coronal, and OrganMNIST_Sagittal, which are from MedMNIST. In the experiments, baselines include the traditional CNN models, automated machine learning (AutoML) methods, and related capsule network methods. The experimental results demonstrate that the proposed AOC-Caps achieves better performance on most of the seven medical image datasets.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/12/5/2634; https://doaj.org/toc/2076-3417
DOI: 10.3390/app12052634
URL الوصول: https://doaj.org/article/3d42180d326a4af7bba889a92514a940
رقم الأكسشن: edsdoj.3d42180d326a4af7bba889a92514a940
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app12052634