Abstract
Melanoma is the deadliest of many different types of skin cancer. Clinical screening is followed by dermoscopic analysis and histopathological examination in the diagnosis of melanoma. Melanoma is a type of skin cancer that is highly curable if caught early. A visual examination of the affected area of the skin is the first step in melanoma skin cancer diagnosis. Dermatologists use a high-speed camera to take dermatoscopic images of skin lesions, which have an accuracy of 65–80% in melanoma diagnosis without any additional technical support. This research shows how to classify skin cancer using skin lesion photos using an automated classification approach based on image processing techniques. By studying images of skin lesions, the classification system will be able to determine whether or not a patient has melanoma. The contribution of this paper includes testing many different backbones and input sizes on the CNN models to evaluate the accuracy of the model on the siim-isic dataset. The overall prediction rate of melanoma diagnosis was raised to 82–86% on Sensitivity.
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References
Carvajal, R.D., Marghoob, A., Kaushal, A., et al.: Melanoma and Other Skin Cancers. Cancer Network (2015) https://www.cancernetwork.com/view/melanoma-and-other-skin-cancers
Habuza, T., Navaz, A., Hashim, F., et al.: AI applications in robotics, diagnostic image analysis and precision medicine: current limitations, future trends, guidelines on CAD systems for medicine. Informatics in Medicine Unlocked 24, 100596 (2021). https://doi.org/10.1016/j.imu.2021.100596
Yu, K.H., Beam, A., Kohane, I.: Artificial intelligence in healthcare. Nature Biomedical Eng. 2, 719–731 (2018)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press (2016)
Huynh, A., Nguyen, B.T., Nguyen, H.T, et al.: A method of Deep Reinforcement Learning for Simulated Autonomous Vehicle Control. In: Proceedings of 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2021), Online streaming (2021).
Nguyen, H.D., Huynh, T., Hoang, S., Pham, V., Zelinka, I.: Language-oriented Sentiment Analysis based on the grammar structure and improved Self-attention network. In: Proceedings of 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), Prague, Czech Public (2020)
Duong, D., Le, Q., Nguyen-Tai, T.L., et al.: An effective AQI estimation using sensor data and stacking mechanism. In: Proceedings of 20th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2021), Cancun, Mexico. FAIA 337, pp. 405418 (2021) IOS Press
Phan, T., Pham, V., Nguyen, H., et al.: Ontology-based resume searching system for job applicants in information technology. In: Proceedings of 34th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2021), Kuala Lumpur, Malaysia. LNAI 12798, pp. 261 – 273 (2021). Springer
Nguyen, D., Nguyen, T., Vu, H., et al.: TATL: task agnostic transfer learning for skin attributes detection. Med. Image Anal. 78, 102359 (2022)
Nguyen, H., Sakama, C.: Feature learning by least generalization. In: Proceedings of the 30th International Conference on Inductive Logic Programming (ILP 2021), Online streaming. LNCS, vol. 13191, pp. 193202 (2021). Springer, Cham
Pham, V., Nguyen, H., Pham, B., et al.: Robust engineering-based unified biomedical imaging framework for liver tumor segmentation. Current Medical Imaging (2022). https://doi.org/10.2174/1573405617666210804151024
Pham, T.C., Doucet, A., Luong, C.M., et al.: Improving skin-disease classification based on customized loss function combined with balanced mini-batch logic and real-time image augmentation. IEEE Access 8, 150725–150737 (2020)
Nguyen, H., Tran, V., Pham, V., et al.: Design a learning model of mobile vision to detect diabetic retinopathy based on the improvement of MobileNetV2. Int. J. Digit. Enterp. Technol. (IJDET) 2(1), 38–53 (2022)
Pham, T.C., Luong, C.M., Hoang, V.D., Doucet, A.: AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized deep CNN architecture. Scientific Reports 11, 17485 (2021)
Ha, Q., Liu, B., Liu, F.: Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge (2020). https://arxiv.org/pdf/2010.05351v1.pdf
Zhang, Y., Wang, C.: SIIM-ISIC melanoma classification with DenseNet. In: Proceedings of the IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2021), pp. 1417, Nanchang, China (2021). https://doi.org/10.1109/ICBAIE52039.2021.9389983
Reis, H.C., Turk, V., Khoshelham, K., Kaya, S.: InSiNet: a deep convolutional approach to skin cancer detection and segmentation. Med Biol Eng Comput 60(3), 643–662 (2022)
Le, T.H.V., Van, H.T., Tran, H.S., Nguyen, P.K., Nguyen, T.T., Le, T.H.: Applying convolutional neural network for detecting highlight football events. In: Cong Vinh, P., Rakib, A. (eds.) ICCASA 2021. LNICSSITE, vol. 409, pp. 300–313. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93179-7_23
Lin, T., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
Rotemberg, V., Kurtansky, N., Betz-Stablein, B., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z
Codella, N., Gutman, D., Celebi, M., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC). In: Proceedings of IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Washington DC, USA (2018)
Combalia, M., Codella, N., Rotemberg, V., et al.: BCN20000: Dermoscopic Lesions in the Wild. (2019) https://core.ac.uk/download/pdf/286456448.pdf
Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161
Olivas, E., Guerrero, J., Sober, M., et al.: Handbook Of Research On Machine Learning Applications and Trends. Information Science Reference (2009)
Gollapudi, S.: Deep learning for computer vision. In: Learn Computer Vision Using OpenCV, pp. 51–69. Apress, Berkeley, CA (2019). https://doi.org/10.1007/978-1-4842-4261-2_3
Nguyen, H.D., Do, N.V., Pham, V.T.: A method for designing knowledge-based systems and application. In: Elgnar, A., et al. (eds.) Applications Computational Intelligence Multi Disciplinary Research, Academic Press, Elsevier (2022)
Scarselli, F., Gori, M., Ah Chung, T., et al.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2009)
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This research is supported by the VNUHCM-University of Information Technology’s Scientific Research Support Fund.
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Huynh, A.T., Hoang, VD., Vu, S., Le, T.T., Nguyen, H.D. (2022). Skin Cancer Classification Using Different Backbones of Convolutional Neural Networks. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_14
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