Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

被引:44
作者
Khishe, Mohammad [1 ]
Caraffini, Fabio [2 ]
Kuhn, Stefan [3 ]
机构
[1] Imam Khomeini Marine Sci Univ Nowshahr, Dept Elect Engn, Nowshahr 1684613114, Iran
[2] De Montfort Univ, Inst Artificial Intelligence, Leicester LE1 9BH, Leics, England
[3] De Montfort Univ, Cyber Technol Inst, Leicester LE1 9BH, Leics, England
关键词
COVID-19; heuristic optimisation; deep convolutional neural networks; chest X-rays; OPTIMIZATION; MODELS; SYSTEM; CLASSIFICATION;
D O I
10.3390/math9091002
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.
引用
收藏
页数:18
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