A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
Source abstract
Abstract
Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cutaneous Melanoma Detection and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China
References (44)
Show source-provided references
1. Keerthana, D., Venugopal, V., Nath, M.K., and Mishra, M. (2023). Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomed. Eng. Adv., 5.10.1016/j.bea.2022.100069 ↗
2. Global Burden of Cutaneous Melanoma in 2020 and Projections to 204010.1001/jamadermatol.2022.0160 ↗
3. Myronenko, A. (2018). 3D MRI brain tumor segmentation using autoencoder regularization. International MICCAI Brainlesion Workshop, Springer International Publishing.10.1007/978-3-030-11726-9_28 ↗
4. A dermatoscope image classification method based on FL-ResNet50
5. A survey on deep learning in medicine: Why, how and when?10.1016/j.inffus.2020.09.006 ↗
6. Computeraided diagnosis of skin cancer: A review10.2174/1573405616666200129095242 ↗
7. Malignant melanoma classification using deep learning:Datasets, performance measurements, challenges and opportunities10.1109/ACCESS.2020.3001507 ↗
8. Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance10.1109/TMI.2017.2695227 ↗
9. Naeem, A., Anees, T., Naqvi, R.A., and Loh, W.-K. (2022). A comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis. J. Pers. Med., 12.10.3390/jpm12020275 ↗
10. A technical review report on deep learning approach for skin cancer detection and segmentation10.1007/978-981-15-8335-3_9 ↗
11. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.10.1007/978-3-319-24574-4_28 ↗
12. A methodological approach to the classification of dermoscopy images10.1016/j.compmedimag.2007.01.003 ↗
13. CDC_Net: Multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays10.1007/s11042-022-13843-7 ↗
14. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
15. An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models
16. Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction10.1007/s10916-019-1413-3 ↗
17. Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval
18. Predicting cervical cancer biopsy results using demographic and epidemiological parameters: A custom stacked ensemble machine learning approach10.1080/23311916.2022.2143040 ↗
19. Khanna, V.V., Chadaga, K., Sampathila, N., Prabhu, S., Bhandage, V., and Hegde, G.K. (2023). A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Appl. Syst. Innov., 6.10.3390/asi6020032 ↗
20. Identification and classification of pigmented skin diseases based on deep convolutional neural network
21. Computer-Aided Diagnosis of Melanoma Using Borderand Wavelet-Based Texture Analysis10.1109/TITB.2012.2212282 ↗
22. Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images10.1016/j.eswa.2017.08.010 ↗
23. Automated melanoma recognition10.1109/42.918473 ↗
24. Bajwa, M.N., Muta, K., Malik, M.I., Siddiqui, S.A., Braun, S.A., Homey, B., Dengel, A., and Ahmed, S. (2020). Computer-aided diagnosis of skin diseases using deep neural networks. Appl. Sci., 10.10.3390/app10072488 ↗
25. Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network10.1063/1.5141652 ↗
26. Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions10.1109/TMI.2013.2239307 ↗
27. Deep learning-based system for automatic melanom a detection10.1109/ACCESS.2019.2962812 ↗
28. Moldovan, D. (2019, January 21–23). Transfer learning based method for two-step skin cancer images classification. Proceedings of the 2019 E-Health and Bioengineering Conference (EHB), Iasi, Romania.10.1109/EHB47216.2019.8970067 ↗
29. Classification of skin lesions in dermatoscopic images with deep convolution network
30. Hasan, M., Barman, S.D., Islam, S., and Reza, A.W. (2019, January 19–22). Skin cancer detection using convolutional neural network. Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence, Bali, Indonesia.10.1145/3330482.3330525 ↗
31. Skin Cancer Classification Using Deep Spiking Neural Network10.1007/s10278-023-00776-2 ↗
32. Kousis, I., Perikos, I., Hatzilygeroudis, I., and Virvou, M. (2022). Deep learning methods for accurate skin cancer recognition and mobile application. Electronics, 11.10.3390/electronics11091294 ↗
33. Ranpreet, K., GholamHosseini, H., Sinha, R., and Lindén, M. (2022). Melanoma classification using a novel deep convolutional neural network with dermoscopic images. Sensors, 22.10.3390/s22031134 ↗
34. Alwakid, G., Gouda, W., Humayun, M., and Sama, N.U. (2022). Melanoma Detection Using Deep Learning-Based Classifications. Healthcare, 10.10.3390/healthcare10122481 ↗
35. Multiclass skin cancer classification using EfficientNets—A first step towards preventing skin cancer10.1016/j.neuri.2021.100034 ↗
36. Majtner, T., Bajić, B., Yildirim, S., Hardeberg, J.Y., Lindblad, J., and Sladoje, N. (2018). Ensemble of convolutional neural networks fordermoscopic images classification. arXiv.
37. Ioffe, S., and Szegedy, C. (2015). Batch normalization accelerating deep network training by reducing internal covariate shift. arXiv.
38. Modulation classification based on signal constellation diagrams and deep learning10.1109/TNNLS.2018.2850703 ↗
39. Focal loss for dense object detection10.1109/TPAMI.2018.2858826 ↗
40. A comparison of transfer learning performance versushealth experts in disease diagnosis from medical imaging10.1109/ACCESS.2020.3004766 ↗
41. Skin cancer disease images classification using deep learning solutions10.1007/s11042-021-10952-7 ↗
42. Melanoma image classification based on MobileNetV2 network10.1016/j.procs.2021.12.132 ↗
43. Attributes based skin lesion detection and recognition: A mask RCNN andtransfer learning-based deep learning framework10.1016/j.patrec.2020.12.015 ↗
44. Multi-site cross-organ calibrated deeplearning (MuSClD): Automated diagnosis of non-melanoma skin cancer10.1016/j.media.2022.102702 ↗