Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur, Taranjit; Gandhi, Tapan Kumar; Panigrahi, Bijaya Ketan

doi:10.1109/jtehm.2021.3077142

Cited by 27 publications

(9 citation statements)

References 34 publications

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“…The results of the experiments for COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia classifications are shared in Tables 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,and 31. In this section, the results are evaluated.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Yaşar

Ceylan

2021

Cogn Comput

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Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral groundglass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.

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Section: Discussionmentioning

confidence: 99%

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Yaşar

Ceylan

2021

Cogn Comput

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“…The planned model is executed in MATLAB, and the successive score was calculated by comparing the key metrics with old models in terms of, accuracy, Precision, sensitivity, error rate, F-measure, and recall. CNN with Fuzzy (CNN-F) [21], Fusion schemes (FC) [22], bat optimization with Fuzzy (BO-F) technique [23], CNN with VGG16 [24], and Hidden Markov with U-net Architecture (HMUA) [25].…”

Section: Comparative Analysismentioning

confidence: 99%

“…State-of-the-art comparisonIt has taken more time to run the entire process Zebari et al[22] FSThe proposal helps the radiologist The determination accuracy depends on the quality of data in KNN Kaur et al[23] BO-F Health care data sets are used to attain correct predictionOverlapping occurs in the target classesHeidari et al [24] CNN and VGG16 Possible to use the large data set High correlation and high matrix dimensionality Marfak et al [25] HMUA Computation time low Inaccurate segmentation Proposed VbANF High accuracy and F1-score, High Precision, High sensitivity, and specificity, lower error rate -…”

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confidence: 99%

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Mary¹,

Kumar

Venkatesan³

et al. 2022

Interdiscip Sci Comput Life Sci

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In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results. Graphical abstract

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“…In 2021, Kaur et al . [ 22 ] have proposed an expert model on the basis of deep features, and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel corona virus. The features were extracted from the fully connected layer of transfer learned MobileNetv2 by the FKNN training.…”

Section: Literature Surveymentioning

confidence: 99%

“…[ 8 ] Deep learning • It permits a reliable and robust analysis for supporting the clinical decision-making process • It permits to classification and investigate the normal, pathological, and COVID-19 cases • Deep Learning in practice is hard and expensive Chen et al . [ 6 ] VGG-16 • It is used for benchmarking on a particular task • It is too slow for training Kaur et al [ 22 ] BAT • It can improve its local search capability and ensure the stability of the algorithm • It requires very huge amount of data to perform better than other methods Wang and Quan [ 7 ] DSAE • It is useful to removes noise from the input signal • It is very expensive to train due to the complex data models …”

Section: Literature Surveymentioning

confidence: 99%

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Das¹

2021

Multimed Tools Appl

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COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed “Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)”. The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

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Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Cited by 27 publications

References 34 publications

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

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