DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images
Abstract
:Simple Summary
Abstract
1. Introduction
- The novel proposed DSCC_Net model is designed to identify four different types of skin cancer. The proposed model has the capability of extracting dominant features from dermoscopy images that can assist in the accurate identification of the disease.
- In this study, we reduce the complexity of the model by decreasing the number of trainable parameters to obtain a significant classifier.
- The CNN model’s accuracy is compromised as a result of the problem of class imbalance in medical datasets. We overcome this issue by using an up-sampling technique, SMOTE Tomek, to obtain concoction samples of the image at each class to gain enhanced accuracy.
- The Grad-CAM heat-map technique is utilized to illustrate the visible features of skin cancer disease classification approaches.
- The proposed model achieved superior results, as compared to six baseline classifiers, Vgg-19, ResNet-152, Vgg-16, MobileNet, Inception-V3, and EfficientNet-B0, in terms of many evaluation metrics, i.e., accuracy, area under the curve (AUC), precision, recall, loss, and F1 score.
- Additionally, the proposed model also produced significant results as compared to the recent state-of-the-art classifiers.
2. Literature Review
3. Materials and Methods
3.1. Proposed Study Flow for the Diagnosis of Skin Cancer
3.2. Dataset Description
3.3. Using SMOTE Tomek to Balance Dataset
3.4. Proposed Model
3.4.1. Structure of the Proposed DSCC_Net
3.4.2. Convolutional Blocks of CNN Model
3.4.3. Flattened Layer
3.4.4. Dropout Layer
3.4.5. Dense Block of Proposed DSCC_Net
- ReLU Function
- Dense Layer
3.5. Model Evaluations
4. Results and Discussion
4.1. Experimental Setup
4.2. Accuracy Compared with Other Models
4.3. AUC Comparison with Other Models
4.4. Compared with Other Models Using Precision
4.5. Compared of DSCC_Net against Other Models Using Recall
4.6. F1-Score Comparison with Recent Deep Model
4.7. Comparison of Proposed Model with Other Models Using Loss
4.8. ROC Compared with Recent Model
4.9. AU(ROC) Extension for Multi-Class Comparison against Recent Models
4.10. Comparison of DSCC_Net with Six Models Using a Confusion Matrix
4.11. Comparison of the Proposed Model with State-Of-The-Art
4.12. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref | Model | Type | Limitations | Dataset | Accuracy |
---|---|---|---|---|---|
[35] | Two hybrid CNN Models | Benign vs. Melanoma. | The classification accuracy of the model may be enhanced by using more advanced sampling techniques and data preparation. | ISBI 2016 | 88.02% |
[36] | Spiking Vgg-13 | Melanoma vs. non-Melanoma. | The model’s interpretability has to be improved. | ISIC 2019 | 89.57% |
[37] | CNN, AlexNet, Vgg-16, Vgg-19 | MEL, BCC, AKIEC, NV, BKL, DF, and VASC. | There is a limited selection of lightweight networks and hyperparameters for evaluation. | HAM10000 | 92.25% |
[29] | Deep CNN | Malignant vs. Benign. | The model’s segmentation performance is fragile to occlusions in skin pictures, and it struggles with low-contrast skin disease images. | ISIC 2016, ISIC 2017, ISIC 2020 | 90.42% |
[38] | CNN and ResNet-50 | MEL, BCC, AKIEC, NV, BKL. | Different models and datasets call for various hyperparameter settings. | HAM10000 | 86% |
[39] | DenseNet-201 | MEL & non-MEL. | To further enhance the model’s generality, a more clinical dataset of skin-cancer cases is required. | ISIC 2019 | 76.08% |
[30] | MobileNet-V2 | Malignant & benign. | Overall accuracy drops when there is a large gap between the data domain and the target domain. | ISIC 2020 | 98.2% |
[40] | EfficientNets B0-B7 | MEL, BCC, AKIEC, NV, BKL, DF, and VASC. | The proposed model was trained and tested on an imbalanced dataset of skin cancer, and it affects the model performance. | HAM10000 | 87.91% |
[31] | DCNN | Benign & malignant. | Due to the small sample size of the datasets used in this study, local optimizations may have been achieved. | HAM10000 | 91.93% |
[41] | ResNet-152, SE-ResNeXt-101, DenseNet-161 | MEL, BCC, AKIEC, NV, BKL, DF, and VASC. | The computational cost was significant, and the system did not take into account all possible skin cancers. | ISIC 2018 | 93% |
[42] | CNN | MEL, BCC, AKIEC, NV, BKL, DF, and VASC. | Classification persists, however, because the model relies on a small quantity of training data and the hazy borders of skin disease pictures. | HAM10000 | 78% |
[43] | DenseNet-121 | MEL, BCC, and AKIEC. | Due to the lack of adversarial training on other skin cancer datasets, the method’s model remains vulnerable. | HAM10000 | 85% |
No. of Classes | Class Name | No. of Images |
---|---|---|
0 | BCC | 510 |
1 | MEL | 1686 |
2 | MN | 2007 |
3 | SCC | 97 |
No. of Classes | Class Name | No. of Images |
---|---|---|
0 | BCC | 2035 |
1 | MEL | 1952 |
2 | MN | 2007 |
3 | SCC | 2018 |
Layer Type | Output Shape | Parameters |
---|---|---|
Input Layer | (None, 150, 150, 3) | 0 |
Block01 | (None, 150, 150, 8) | 224 |
Block02 | (None, 75, 75, 16) | 1168 |
Block03 | (None, 37, 37, 32) | 4640 |
Block04 | (None, 18, 18, 64) | 18,496 |
Block05 | (None, 9, 9, 128) | 73,856 |
Dropout_1 | (None, 4, 4, 128) | 0 |
Flatten | (None, 2048) | 0 |
Dense_1 | (None, 512) | 1,049,088 |
ReLu | (None, 512) | 0 |
Dense_2 | (None, 4) | 2052 |
Output: SoftMax | (None, 4) | 0 |
Total Parameters: | 1,149,524 | |
Trainable Parameters: | 1,149,524 | |
Non-Trainable Parameters: | 0 |
Classifiers | Accuracy | Precision | Recall | F1-Score | AUC |
---|---|---|---|---|---|
Vgg-16 | 91.12% | 92.09% | 90.43% | 91.13% | 99.02% |
Vgg-19 | 91.68% | 92.23% | 90.57% | 91.71% | 98.14% |
MobileNet | 92.51% | 92.95% | 91.40% | 92.17% | 98.75% |
ResNet-152 | 89.32% | 90.73% | 88.21% | 89.27% | 98.74% |
EfficientNet-B0 | 89.46% | 90.21% | 88.21% | 89.31% | 98.43% |
Inception-V3 | 91.82% | 92.28% | 91.12% | 91.76% | 99.06% |
Proposed Model (With SMOTE Tomek) | 94.17% | 94.28% | 93.76% | 93.93% | 99.43% |
Proposed Model (Without SMOTE Tomek) | 83.20% | 85.01% | 80.62% | 58.09% | 96.65% |
Ref | Year | Model | Datasets | Accuracy | Recall | Precision | F1-Score |
---|---|---|---|---|---|---|---|
[70] | 2023 | CNN | ISIC-2017 | 92.00% | 91.90% | 91.65% | 91.99% |
[71] | 2023 | Vgg-13 | ISIC-2019, Derm-IS | 89.57% | 90.70% | 89.66% | 89.65% |
[72] | 2023 | Deep Belief Network | HAM-10000 | 93.00% | 92.91% | 92.45% | 92.65% |
[73] | 2021 | ConvNet | ISIC-2018, Derm-IS | 86.90% | 86.14% | 87.47% | - |
[74] | 2022 | 2D superpixels + RCNN | HAM-10000 | 85.50% | 83.40% | 84.50% | 85.30% |
[75] | 2021 | ResNeXt101 | ISIC-2019 | 88.50% | 87.40% | 88.10% | 88.30% |
[76] | 2022 | SCDNet | ISIC-2019 | 92.91% | 92.18% | 92.19% | 92.18% |
Ours | - | DSCC_Net with SMOTE Tomek | ISIC-2020, Derm-IS, HAM-10000 | 94.17% | 94.28% | 93.76% | 93.93% |
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Tahir, M.; Naeem, A.; Malik, H.; Tanveer, J.; Naqvi, R.A.; Lee, S.-W. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers 2023, 15, 2179. https://doi.org/10.3390/cancers15072179
Tahir M, Naeem A, Malik H, Tanveer J, Naqvi RA, Lee S-W. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers. 2023; 15(7):2179. https://doi.org/10.3390/cancers15072179
Chicago/Turabian StyleTahir, Maryam, Ahmad Naeem, Hassaan Malik, Jawad Tanveer, Rizwan Ali Naqvi, and Seung-Won Lee. 2023. "DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images" Cancers 15, no. 7: 2179. https://doi.org/10.3390/cancers15072179
APA StyleTahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S. -W. (2023). DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers, 15(7), 2179. https://doi.org/10.3390/cancers15072179