MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Gopatoti, Anandbabu; Vijayalakshmi, P.

doi:10.1016/j.bspc.2023.104857

Cited by 7 publications

(1 citation statement)

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“…In the residual unit, the problems of gradient disappearance and gradient explosion during deep neural network training are solved by introducing skip connection. Gopatoti et al [14] propose a multi-textural multi-class attention recurrent residual convolutional neural network, it can classify the CXR (chest X-ray) images into normal, COVID-19, viral pneumonia, and lung opacity using extracted multi-textural features with improved accuracy. Zhang et al [15] propose a dimensiondriven multi-path attention residual network, a dimension-driven multipath attention residual block is developed to effectively obtain the multi-scale features, and differently treats these features containing different amounts of information through the channel attention mechanism, which makes the data depth features better expressed.…”

Section: Pneumonia Classification Methods Based On Residual Unitmentioning

confidence: 99%

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Zhou,

Guo,

Peng

et al. 2024

CMC

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Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the Multi-scale Directional Feature Perception Module (MDFPM) is designed, which uses a threebranch structure of different sizes convolution to transmit direction feature layer by layer, and focuses on the target region to enhance the feature information. Thirdly, the Axial Compression Former Module (ACFM) is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net, comparative experiments and ablation experiments are carried out. In the COVID-19 RADIOGRAPHY DATABASE, the Accuracy, Recall, Precision, F1 Score, and Specificity of MSD-Net are 97.76%, 95.57%, 95.52%, 95.52%, and 98.51%, respectively. In the chest X-ray dataset, the Accuracy, Recall, Precision, F1 Score and Specificity of MSD-Net are 97.78%, 95.22%, 96.49%, 95.58%, and 98.11%, respectively. This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

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Section: Pneumonia Classification Methods Based On Residual Unitmentioning

confidence: 99%