Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

Verma, Pratima; Tripathi, Vikas; Pant, Bhaskar

doi:10.1016/j.matpr.2021.02.244

Cited by 19 publications

(10 citation statements)

References 23 publications

(17 reference statements)

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“…However, this rate was reported as 84.2% for three class classification. Poonam et al 34 made a COVID, non‐COVID classification with the method they applied using the transfer learning approach. The classification accuracy achieved was 90.45% in the respective dataset.…”

Section: Discussionmentioning

confidence: 99%

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The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

Cengil

Çınar

2021

Int J Imaging Syst Tech

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In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

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Section: Discussionmentioning

confidence: 99%

“…In Ref. [ 34 ], different learning rates have been tried to eliminate the overfitting problem. In addition, it has been proposed to use the transfer learning method and the two‐layer CNN community to identify COVID‐19 images.…”

Section: Introductionmentioning

confidence: 99%

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

Cengil

Çınar

2021

Int J Imaging Syst Tech

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“…The literature research shows promising results obtained in CNN-based studies with CXR images. [16][17][18][19][20][21][22][23][24][25][26][27][28][29] In these studies, pretrained CNN models that were generally based on transfer learning were used. Additionally, many researchers have focused on accuracy scores and the dual classification of COVID-19 cases as either positive or negative.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…Their research achieved an accuracy score of 99.18% in the experimental studies. Reference 25 , VGG‐16, VGG‐19, ResNet50, and Inception V3 CNN models have tried different learning rates to eliminate the effects of the overfitting problem. Also, for a faster classification, the use of the two‐layer CNN model in the transfer learning method has been proposed.…”

Section: Introductionmentioning

confidence: 99%

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

Aslan

2022

Int J Imaging Syst Tech

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COVID‐19 has emerged as a global pandemic affecting the world, and its adverse effects on society still continue. So far, about 243.57 million people have been diagnosed with COVID‐19, of which about 4.94 million have died. In this study, a new model, called COVIDetNet, is proposed for automated COVID‐19 detection. A lightweight CNN architecture trained instead of the popular and pretrained convolution neural network (CNN) models such as VGG16, VGG19, AlexNet, ResNet50, ResNet100, and MobileNetV2 from scratch with chest x‐ray (CXR) images was designed. A new feature set was created by concatenating the features of all layers of the designed CNN architecture. Then, the most efficient features chosen among the features concatenating with the Relief feature selection algorithm were classified using the support vector machine (SVM) method. The experimental works were carried out on a public COVID‐19 CXR database. Experimental results demonstrated 99.24% accuracy, 99.60% specificity, 99.39% sensitivity, 99.04% precision, and an F1 score of 99.21%. Also, in comparison to AlexNet and VGG16 models, the deep feature extraction durations were reduced by approximately 6‐fold and 38‐fold, respectively. The COVIDetNet model provided a higher accuracy score than state‐of‐the‐art models when compared to multi‐class research studies. Overall, the proposed model will be beneficial for specialist medical staff to detect COVID‐19 cases, as it provides faster and higher accuracy than existing CXR‐based approaches.

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“…Among all classification models, the ResNet50 gives an average accuracy of 97% [21]. The pre-processed images followed by state-of-art DL models for the classification of data have been suggested by some researchers and the accuracy of positive and negative patients has also been calculated [22].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Kaur

Harnal

Tiwari

et al. 2021

IJERPH

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COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

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Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

Cited by 19 publications

References 23 publications

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

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