Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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Classification of electrocardiogram (ECG) data using deep learning methods

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ◽

10.1109/ismsit50672.2020.9255000 ◽

2020 ◽

Author(s):

Fatma Bozyigit ◽

Fatih Erdemir ◽

Murat Sahin ◽

Deniz Kilinc

Keyword(s):

Deep Learning ◽

Learning Methods ◽

Electrocardiogram Ecg ◽

Ecg Data

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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