Concat_CNN: A Model to Detect COVID-19 from Chest X-ray Images with Deep Learning

Saha, Priyanka; Neogy, Sarmistha

doi:10.1007/s42979-022-01182-1

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…The authors of [30] proposed a concatenation-based CNN (Concat_CNN) model to detect COVID-19 from chest X-rays images. A comparison was made between Concat_CNN and the following transfer models: VGG16, InceptionV3, Resnet50, and DenseNet121.…”

Section: The Detection Of Covid-19 Using Chest X-raysmentioning

confidence: 99%

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Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

et al. 2023

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The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.

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Section: The Detection Of Covid-19 Using Chest X-raysmentioning

confidence: 99%

“…In [29], the authors indicated that COVID-Net has a recorded accuracy at 92.4 A. In [30], the authors proposed Concat_CNN, which recorded A = 96.31, P = 95.8, and R = 92.99. In [32], a concatenated CNN model was proposed and recorded A = 98.02, F1 = 98.24, P = 97.04, and R = y.…”

Section: Comparison With Literature Studiesmentioning

confidence: 99%

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

et al. 2023

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“…To demonstrate that it can classify well given the distribution of different datasets, the trained model required to be validated. The proposed model compared to pre-trained models contains ResNet50-v2 [8] and Inception-v3 [19]. Figure 5 shows the performance comparison of the CNN models.…”

Section: B Model Performancementioning

confidence: 99%

Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network

Iklima

Kadarina

Priambodo

2022

j.electron.electromedical.eng.med.inform

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A Convolutional Neural Network (CNN) classifier is generally utilized to classify an image tensor according to the mapped labels. The simplification of the classifier causes CNN to be often used to classify images, especially in the biomedical field. Thus, CNN is widely used to classify computer tomography (CT) and chest X-ray (CXR) images against the mapped labels. Several transfer learning models were implemented to classify CXR images for preliminary detection of COVID-19 infection, e.g., ResNet, Inception, Xception, etc. However, a transfer learning model has a maximum and minimum input resolution. Thus, the computational cost tends to be huge and unable to be optimized. Therefore, A custom CNN model can be a solution to reduce computational costs by configuring the feature extraction layers. This study proposed an efficient reduction of feature extraction for COVID-19 CXR namely Depthwise Separable Convolution Network. Furthermore, numerous strategies were adopted to lower the computational cost while retaining accuracy, including customizing the Batch Normalization (BN) layer and replacing the convolution layer with a separable convolution layer. The proposed model successfully reduced the feature extraction represented by the decreases in trainable parameters from 28.640 trainable parameters to 4.640 trainable parameters. The depthwise separable convolution effectively retains the performance accuracy 72.96%, loss 12.43%, recall 74.67%, precision 77.67%, and F1-score 75.33%. The CXR augmentation is also successfully increase the performance accuracy 74.55%, loss 11.37%, recall 77.67%, precision 79.56%, and F1-score 78.33%.

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FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images

S.,

2024

Med Biol Eng Comput

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Concat_CNN: A Model to Detect COVID-19 from Chest X-ray Images with Deep Learning

Cited by 9 publications

References 33 publications

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images

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