Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models
Abstract
:1. Introduction
- Improved accuracy and robustness in skin cancer detection and classification
- Enhanced generalization capabilities, enabling accurate identification of skin cancer
- A comparative examination indicating the superiority of the suggested concatenated model over individual MobileNetV2 and Xception models, as well as other existing approaches
- Real-world application with a user-friendly interface for efficient and reliable skin cancer screening
2. Related Works
3. Materials and Methods
3.1. Dataset Description
3.2. Methodology
3.2.1. Motivation
3.2.2. Image Augmentation and Preprocessing
3.2.3. Deep Learning (DL)
3.2.4. SkinNetX Model
3.3. Transfer Learning (TL)
3.3.1. Xception Model
3.3.2. MobileNetV2
3.3.3. AlexNet Model
3.3.4. DenseNet121 Model
3.3.5. InceptionV3 Model
3.4. Hyperparameter Setting
3.5. Performance Evaluation Metrics
4. Results and Discussion
4.1. Performance Evaluation of SkinNetX Model
4.2. Comparative Evaluation Using Cutting-Edge Pre-Trained DL Models
4.3. Comparative Studies with Recent Research
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Layer Type | Number of Filters |
---|---|
MobileNetV2 | |
GlobalAveragePooling2D | |
Dense | 256 |
Dropout | 0.5 |
Dense | 512 |
Xception | |
GlobalAveragePooling2D | |
Dense | 256 |
Dropout | 0.5 |
Dense | 512 |
Concatenate | |
Dense | 2 |
Model | Depth | Parameters (M) | Size (MB) |
---|---|---|---|
AlexNet | 11 | 60 | 227 |
DenseNet121 | 242 | 8.1 | 33 |
MobileNetV2 | 105 | 3.5 | 14 |
Xception | 81 | 22.9 | 88 |
InceptionV3 | 189 | 23.9 | 92 |
ResNet50V2 | 103 | 25.6 | 98 |
Parameter | Values |
---|---|
Learning rate | 0.0001 |
Optimizer | SGD |
Epochs | 100 |
Verbose | 1 |
Activation function | ReLU |
Iteration per epoch | 12 |
Early stopping | Patience = 80 |
Model | Accuracy | Precision | Recall | F1-Score | Misclass | AUC |
---|---|---|---|---|---|---|
Proposed Model | 97.56 | 93.33 | 100 | 96.55 | 0.0244 | 97.00 |
Xception | 85.37 | 93.33 | 73.68 | 82.35 | 0.1463 | 96.00 |
MobileNetV2 | 90.24 | 93.33 | 82.35 | 87.50 | 0.0976 | 95.00 |
AlexNet | 80.49 | 80.00 | 70.59 | 75.00 | 0.1951 | 92.00 |
InceptionV3 | 82.93 | 70.00 | 93.33 | 80.00 | 0.1707 | 91.00 |
DenseNet121 | 95.12 | 93.33 | 93.33 | 93.33 | 0.0488 | 95.00 |
ResNet50V2 | 77.50 | 92.86 | 61.90 | 74.29 | 0.2250 | 86.00 |
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Ogundokun, R.O.; Li, A.; Babatunde, R.S.; Umezuruike, C.; Sadiku, P.O.; Abdulahi, A.T.; Babatunde, A.N. Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models. Bioengineering 2023, 10, 979. https://doi.org/10.3390/bioengineering10080979
Ogundokun RO, Li A, Babatunde RS, Umezuruike C, Sadiku PO, Abdulahi AT, Babatunde AN. Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models. Bioengineering. 2023; 10(8):979. https://doi.org/10.3390/bioengineering10080979
Chicago/Turabian StyleOgundokun, Roseline Oluwaseun, Aiman Li, Ronke Seyi Babatunde, Chinecherem Umezuruike, Peter O. Sadiku, AbdulRahman Tosho Abdulahi, and Akinbowale Nathaniel Babatunde. 2023. "Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models" Bioengineering 10, no. 8: 979. https://doi.org/10.3390/bioengineering10080979
APA StyleOgundokun, R. O., Li, A., Babatunde, R. S., Umezuruike, C., Sadiku, P. O., Abdulahi, A. T., & Babatunde, A. N. (2023). Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models. Bioengineering, 10(8), 979. https://doi.org/10.3390/bioengineering10080979