DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images
Abstract
:1. Introduction
- Using a more sophisticated methodology for diagnosis and identifying COVID-19 patients through a deep learning approach, which is based on X-ray images.
- Develo** and examining a deep learning model to automatically detect and early diagnose COVID-19-infected patients in an effective manner.
- Performing an experimental analysis of the proposed model while classifying COVID-19 cases by using operable X-ray images. It has a lower implementation cost than other existing models that are trained using CT scan.
- Classifying COVID-19 images as four classes, three classes and binary classes: namely COVID-19, normal, bacterial, and viral pneumonia from normal and regular pneumonia.
- Demonstrating comparative performance analysis of the proposed work with the other previous state-of-the-art works and Grad-CAM-based visualization marking the most flawless classification results of the COVID-19 cases using the datasets of chest X-ray images.
2. Related Background
3. Methodology
3.1. Data Preprocessing
3.2. Data Augmentation
3.3. Transfer Learning with Convolutional Neural Networks
3.3.1. Convolutional Layer
3.3.2. Pooling Layer
3.3.3. Fully Connected Layer
3.4. Proposed Architecture with Six Extra Layers
4. Experimental Results
4.1. Dataset Description
4.2. Experimental Configuration and Implementation
4.3. Evaluation Metrics
4.4. Normalization of Confusion Matrix
4.5. Classification Performance on Dataset-1
4.6. Classification Performance on Dataset-2
4.7. K-Fold Cross Validation
4.8. Classification Performance on Dataset-3
5. Discussion
Discriminative Visualization by Proposed Model Using Grad-Cam
- By considering the normal X-rays, there is no opacity in normal X-rays, which distinguishes normal cases from all types of pneumonia cases who have opacities with various types [81,82,83]. For normal X-rays, no substantial region is localized, as shown in Figure 13. Since it is more recognizable, it is easier to distinguish from the other patients.
- When looking at the heatmaps for classical viral pneumonia, it is observed that our proposed model has localized the regions with bilateral multifocal Ground Glass Opacities (GGO) as well as patchy consolidations in some cases. These localized characteristics are also commonly recognized as radiological features of classic viral pneumonia [9,84,85].
- Localized activation heatmaps in the event of bacterial pneumonia generally involve opacities with consolidation on the lower and upper lobes. Furthermore, both unilateral and bilateral, as well as peripheral, participation is seen. These characteristics, according to [81,82], are primarily associated with bacterial pneumonia.
- According to [9,85], COVID-19 and typical viral pneumonia have many similarities, including bilateral GGOs and patchy consolidations. Peripheral and diffuse distributions, vascular thickening, micro-reticular opacity, and typical viral-like Ground Glass Opacities (GGOs) are some of the more likely hallmarks of COVID-19-induced pneumonia [86,87]. By closely inspecting the generated heatmap from several COVID-19 infected X-rays (Figure 13), it is observed that the opacities are distributed in a peripheral and diffuse manner. Furthermore, vascular thickening, as well as other conventional viral features, is localized in some of the cases.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author (Month, Year) | Number of Cases and Image | Training Model | VisualizeUsing Gard CAM/Other | Accuracy (%) |
---|---|---|---|---|
Krishnamraju K [45] | 1000 COVID-19 and 1000 normal | VGG16+ MobileNet | No | 97 |
Mousavi Z [46] | 939 healthy cases, 800 COVID-19 and 942 viral pneumonia | Developed LSTM network | No | 90 |
Luz [47] | 1000 COVID-19, 1000 normal and 1000 pneumonia | Efficient deep learning model | Yes | 93.9 |
Al-Waisy [48] | 400 COVID-19 and 400 normal | COVID-CheXNet | Yes | 99.99 |
Aslan [59] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | mAlexNet +BiLSTM | No | 98.14, for first architecture and 98.70 |
Chen [49] | 3 datasets total 513 COVID-19 and1984 non-COVID-19 | VGG16 | Yes | 98 |
Wang [50] | 266 COVID-19, 8,066 normal and 5,538 pneumonia | COVID-Net | Yes | 93.30 |
Gupta [51] | 361 COVID-19, 1345 pneumonia and 1341 normal | InstaCovNet-19 | Yes | 99.08 for 3 class and 99.53 for 2 class |
Arsenovic [60] | 434 COVID-19, 1100 normal and 1100 bacterial pneumonia | ResNetCOVID-19 | No | 94.10 |
Ammar [52] | 150 COVID-19, 150 normal, and 150 pneumonia | MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16 and DenseNet12 | No | Highest accuracy 91.28 for MobileNetV2 |
Jain [53] | 490 COVID-19, 1345 normal and 3632 pneumonia | Xception net, Inception net V3 and ResNeXt, | No | Highest accuracy 97.97 for Xception |
Mohammadi R [54] | 181 COVID-19 and 364 normal | pre-trained VGG16, InceptionResNetV2, MobileNet and VGG19 | No | Highest accuracy 99.1 MobileNet |
Chowdhury [61] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | PDCOVIDNet | Yes | 96.58 |
Turkoglu [55] | 219 COVID-19, 1583 normal and 4290 pneumonia | COVIDetectioNet | No | 99.18 |
Makris [62] | 112 COVID-19, 112 normal and 112 pneumonia | 9 well-known pre-trained CNN model | No | 95 for the best two model (Vgg16 and Vgg19) |
Ouchicha [56] | 1341 normal, 219 COVID-19 and 1345 viral pneumonia | CVDNet | No | 96.69 |
Civit-Masot [63] | 132 COVID-19, 132 healthy and 132 pneumonia | VGG16 | No | 86.00 |
Mahmud [64] | 1583 normal, 305 COVID-19, 1493 viral pneumonia, 2780 bacterial pneumonia | CovXNet | Yes | 90.2 accuracy for four class |
Khan [57] | 1203 normal, 290 COVID-19 931 viral pneumonia, 660 bacterial pneumonia | CoroNet | No | Overall accuracy of 89.6 |
Ozturk [20] | 125 COVID-19 cases, 500 no findings, 500 pneumonia cases | DarkCovid-Net | Yes | 98.08 for two class and 87.02 |
Notations | Definition |
---|---|
I | denotes as input matrix (e.g., image) |
K | represents 2D filter by considering dimension m × n |
F | output of 2D characteristic map |
I*K | operation of convolution |
x | represents as input vector |
Z | represents as output vector |
Train_acc | represents as training accuracy |
Val_acc | represents as validation accuracy |
pneumonia_bac | represents bacterial pneumonia cases |
pneumonia_vir | represents viral pneumonia cases |
Input Layer | Output Shape | Number of Trainable |
---|---|---|
(Depth*Height*Weight) | Parameters | |
Input image | (3*224*224) | 0 |
Conv1 | (64*112*112) | 9472 |
Maxpool1 | (64*56*56) | 0 |
Conv2 | (256*28*28) | 16,640 |
Maxpool2 | (256*28*28) | 0 |
Conv3 | (512*14*14) | 66,048 |
Maxpool3 | (512*14*14) | 0 |
Conv4 | (1024*7*7) | 263,168 |
Maxpool4 | (1024*7*7) | 0 |
Conv5 | (2048*7*7) | 1,050,624 |
FC (Batch_normalization) | (2048*7*7) | 8192 |
FC (ReLu) | (2048*7*7) | 0 |
“Concatanated layers” | - | - |
FC1 (Flatten) | (100,352) | 0 |
FC2 (Dropout) | (100,352) | 0 |
FC3 (Flatten) | (100,352) | 0 |
FC4 (Dropout) | (100,352) | 0 |
FC5 (ReLu) | (256) | 25,690,368 |
FC6 (SoftMax) | (4) | 1028 |
Subset | COVID-19 | Normal | Viral Pneumonia | Bacterial Pneumonia |
---|---|---|---|---|
Training Data | 686 | 690 | 690 | 690 |
Test Data | 457 | 460 | 460 | 460 |
Total | 1143 | 1150 | 1150 | 1150 |
Subset | COVID-19 | Normal | Viral Pneumonia | Bacterial Pneumonia |
---|---|---|---|---|
Training Data | 1143 | 1150 | 1150 | 1150 |
Test Data | 457 | 460 | 460 | 460 |
Total | 1600 | 1610 | 1610 | 1610 |
Class | Task | Precision (%) | Recall (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|---|
Four | COVID-19 | 99.0 | 94.0 | 97.0 | 88.79 |
Normal | 94.0 | 91.0 | 92.0 | ||
Bacterial Pneumonia | 80.0 | 94.0 | 87.0 | ||
Viral Pneumonia | 83.0 | 77.0 | 80.0 | ||
COVID-19 | 1.00 | 96.0 | 98.0 | ||
Three | Normal | 95.0 | 99.0 | 97.0 | 97.22 |
Bacterial Pneumonia | 97.0 | 96.0 | 97.0 | ||
COVID-19 | 1.00 | 97.0 | 98.0 | ||
Three | Normal | 86.0 | 98.0 | 91.0 | 93.11 |
Viral Pneumonia | 95.0 | 85.0 | 90.0 | ||
COVID-19 | 99.0 | 95.0 | 97.0 | ||
Three | Viral Pneumonia | 87.0 | 97.0 | 91.0 | 92.10 |
Bacterial Pneumonia | 95.0 | 85.0 | 90.0 | ||
Two | COVID-19 | 99.0 | 99.0 | 99.0 | 99.13 |
Normal | 99.0 | 99.0 | 99.0 | ||
Two | COVID-19 | 1.00 | 97.0 | 98.0 | 98.47 |
Bacterial Pneumonia | 97.0 | 1.00 | 98.0 | ||
Two | COVID-19 | 1.00 | 98.0 | 99.0 | 98.91 |
Viral Pneumonia | 98.0 | 1.00 | 99.0 |
Dataset | Class | Task | Precision (%) | Recall (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|---|---|
Dataset-1 | Four | COVID-19 | 99.0 | 94.0 | 97.0 | 88.79 |
Normal | 94.0 | 91.0 | 92.0 | |||
B. Pneumonia | 80.0 | 94.0 | 87.0 | |||
V. Pneumonia | 83.0 | 77.0 | 80.0 | |||
Dataset-2 | Four | COVID-19 | 1.00 | 1.00 | 1.00 | 99.46 |
Normal | 1.00 | 1.00 | 1.00 | |||
B. Pneumonia | 1.00 | 98.0 | 99.0 | |||
V. Pneumonia | 98.0 | 1.00 | 99.0 |
Class | Dataset-01 | |||
---|---|---|---|---|
Train_acc | Val_acc | Train_loss | Val_loss | |
Three (COVID vs. pneu_vir vs. normal) | 0.9912 | 0.9311 | 0.0088 | 0.0689 |
Two (COVID vs. pneu_vir) | 0.9935 | 0.9891 | 0.0109 | 0.0065 |
Two (COVID vs. pneu_bac) | 0.9974 | 0.9847 | 0.0026 | 0.0153 |
Folds | Precision(%) | Recall(%) | F1-Score%) | Accuracy%) |
---|---|---|---|---|
Fold-1 | 89.25 | 88.75 | 88.5 | 88.80 |
Fold-2 | 91.5 | 91.75 | 91.75 | 91.64 |
Fold-3 | 88.75 | 88.75 | 88.5 | 88.59 |
Fold-4 | 92.25 | 91.75 | 91.75 | 91.73 |
Fold-5 | 88.5 | 88.25 | 87.75 | 88.06 |
Average | 90.05 | 89.85 | 89.65 | 89.76 |
Folds | Precision | Recall | F1-Score | Accuracy(%) |
---|---|---|---|---|
Fold-1 | 0.985 | 0.985 | 0.985 | 98.48 |
Fold-2 | 0.99 | 0.99 | 0.99 | 98.91 |
Fold-3 | 0.975 | 0.975 | 0.98 | 97.61 |
Fold-4 | 0.985 | 0.985 | 0.98 | 98.04 |
Fold-5 | 0.985 | 0.985 | 0.98 | 98.26 |
Average | 0.984 | 0.984 | 0.983 | 98.26 |
Model | Class | Precision (%) | Recall (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|---|
VGG19 | 4 | 90 | 88.5 | 89 | 88.98 |
ResNet50 | 4 | 88 | 87.25 | 87.5 | 87.5 |
InceptionV3 | 4 | 87.5 | 87.25 | 87.5 | 87.39 |
VGG19 | 3 | 93.25 | 94 | 93 | 94.35 |
ResNet50 | 3 | 95 | 93.5 | 94 | 94.88 |
InceptionV3 | 3 | 93 | 92.25 | 92.75 | 93.27 |
VGG19 | 2 | 99 | 98.5 | 98 | 98.5 |
ResNet50 | 2 | 99 | 98 | 98 | 98.33 |
InceptionV3 | 2 | 0.98 | 97 | 98 | 97.67 |
Proposed model (on Dataset-1) | 4 | 90.05 | 89.85 | 89.65 | 89.76 |
3 | 0.97 | 89.85 | 0.9633 | 97.22 | |
2 | 0.99 | 89.85 | 0.99 | 99.13 | |
Proposed model (on Dataset-2) | 4 | 99.5 | 99.4 | 99.5 | 99.46 |
Proposed model (on Dataset-3) | 2 | 98.4 | 98.4 | 98.3 | 98.26 |
Author | Architecture | Number of Images | Class | Accuracy (%) |
---|---|---|---|---|
Khan et al. [57] | CoroNet | 297 COVID-19, 330 bacterial pneumonia, 310 normal, 327 viral pneumonia images. | 4 | 89.6 |
3 | 95 | |||
2 | 99 | |||
Mahmud et al. [64] | CovXNet | 305 COVID-19, 305 bacterial pneumonia, 305 normal, 305 viral pneumonia images. | 4 | 90.3 |
3 | 89.6 | |||
2 | 94.7 | |||
Ammar et al. [52] | 6 pre-trained models | 150 COVID-19, 150 normal, 150 pneumonia images. | 3 | 91.28 |
Mousavi Z et al. [46] | Developed LSTM network | 800 COVID-19, 942 viral pneumonia, 939 healthy cases images. | 3 | 90.0 |
Arsenovic et al. [60] | ResNetCOVID-19 | 434 COVID-19, 1100 normal, 1100 bacterial pneumonia. | 3 | 94.1 |
Hemdan et al. [79] | COVIDXNet | 25 COVID-19 and 25 normal images. | 2 | 90 |
Sethy et al. [58] | ResNet50 plus SVM | 25 COVID-19 and 25 non-COVID-19. | 2 | 95.38 |
Proposed model (Dataset-1) | Tuned ResNet50V2 | 1143 COVID-19, 1150 viral pneumonia, 1150 bacterial pneumonia, 1150 normal images. | 4 | 89.76 |
3 | 97.22 | |||
2 | 99.13 | |||
Proposed model (Dataset-2) | Tuned ResNet50V2 | 1143 COVID-19, 1150 viral pneumonia, 1150 bacterial pneumonia, 1150 normal images. | 4 | 99.46 |
Proposed model (Dataset-3) | Tuned ResNet50V2 | 1143 COVID-19, 1150 adult pneumonia. | 2 | 98.26 |
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Ahamed, M.K.U.; Islam, M.M.; Uddin, M.A.; Akhter, A.; Acharjee, U.K.; Paul, B.K.; Moni, M.A. DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images. Diagnostics 2023, 13, 551. https://doi.org/10.3390/diagnostics13030551
Ahamed MKU, Islam MM, Uddin MA, Akhter A, Acharjee UK, Paul BK, Moni MA. DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images. Diagnostics. 2023; 13(3):551. https://doi.org/10.3390/diagnostics13030551
Chicago/Turabian StyleAhamed, Md. Khabir Uddin, Md Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter, Uzzal Kumar Acharjee, Bikash Kumar Paul, and Mohammad Ali Moni. 2023. "DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images" Diagnostics 13, no. 3: 551. https://doi.org/10.3390/diagnostics13030551