Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network

Muralidharan, Neha; Gupta, Shaurya; Prusty, Manas Ranjan; Tripathy, Rajesh Kumar

doi:10.1016/j.asoc.2022.108610

Cited by 33 publications

(14 citation statements)

References 38 publications

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“…Bai et al [ 88 ] proposed a multi-feature dictionary representation and ensemble learning method based on symbolic aggregate approximation, which is different from extracting diverse-shapelet for early classification of time series [ 89 ]. Muralidharan et al [ 90 ] adopted multiscale deep CNN with different combinations of modes, which is combined and merged by the fully connected layer for all features. He et al [ 91 ] put forward an integrated framework COVIDNet, regarding ResNet [ 92 ] as the backbone and the spatial pyramid pooling(SSP) to enhance the middle-level features extraction; and the NetVLAD was employed to aggregate features from the low-level and context gating for learning.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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“…Further, the multi-model scenario is found to be more beneficial to reliably identify the emotional states of the subject. A statistical significance test was performed to show that the accuracy improvement in the multimodal emotion recognition model is significant [ 56 ]. An analysis of variance (ANOVA) test was performed on the 5-fold accuracies of different modalities, such as EEG, ECG, and multimodal.…”

Section: Discussionmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

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“…COVID-19 samples with deep features conveyed to the SVM algorithm were classified with an overall 94.7% accuracy. Muralidharan et al [26] utilized a new deep-learning approach for automated ILD detection from X-ray images. First, X-ray image levels containing seven modes were tuned with a wavelet transform-based algorithm.…”

Section: Related Workmentioning

confidence: 99%

Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach

Akbulut

2023

Diagnostics

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Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets.

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Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network

Cited by 33 publications

References 38 publications

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach

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