Improving Skin Lesion Classification and Prediction through Data Augmentation for Enhanced Accuracy
Keywords:
5 layered CNN, ISIC archive, Skin lesion, Malignancy, AugmentationAbstract
Early identification of skin cancer is crucial for improved survival rates, emphasizing the need for accurate computer-aided systems in the diagnostic process. This paper presents a deep learning-based system designed for efficient classification and prediction of skin cancer through the analysis of skin lesions. The proposed system utilizes datasets; HAM10000 collected from the ISIC archive and addresses the class imbalance problem within the skin lesion classes through augmentation techniques. The balanced and processed dataset is used to train and test a fine-tuned 5-layered CNN model with optimized parameters. The evaluation of the model's performance is determined solely by its accuracy and other performance metrics specific to the skin lesion classes. Furthermore, the article includes a comparative analysis and visualization of the balanced and unbalanced datasets to provide insights into their characteristics. The proposed deep learning system offers promising potential for enhancing skin cancer diagnosis by accurately classifying skin lesions and predicting malignancy.
Downloads
References
Woo YR, Cho SH, Lee JD, Kim HS. The Human Microbiota and Skin Cancer. International Journal of Molecular Sciences. 2022; 23(3):1813. https://doi.org/10.3390/ijms23031813
Akyel C, Arıcı N. LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer. Mathematics. 2022; 10(5):736. https://doi.org/10.3390/math10050736
Ünver, Halil Murat, and Enes Ayan. "Skin lesion segmentation in dermoscopic images with combination of YOLO and grabcut algorithm." Diagnostics 9, no. 3 (2019): 72.
McNoe, Bronwen M., Kate C. Morgaine, and Anthony I. Reeder. "Effectiveness of Sun Protection Interventions Delivered to Adolescents in a Secondary School Setting: A Systematic Review." Journal of skin cancer 2021 (2021).
Alom, Md Zahangir, Theus Aspiras, Tarek M. Taha, and Vijayan K. Asari. "Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convolutional networks." arXiv preprint arXiv:1904.11126 (2019).
Kadampur, Mohammad Ali, and Sulaiman Al Riyaee. "Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images." Informatics in Medicine Unlocked 18 (2020): 100282.
Senan, Ebrahim Mohammed, and Mukti E. Jadhav. "Classification of dermoscopy images for early detection of skin cancer–a review." International Journal of Computer Applications 975 (2019): 8887.
Gillmann, Christina, Dorothee Saur, and Gerik Scheuermann. "How to deal with Uncertainty in Machine Learning for Medical Imaging?." In 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), pp. 52-58. IEEE, 2021.
Haggenmüller, Sarah, Roman C. Maron, Achim Hekler, Jochen S. Utikal, Catarina Barata, Raymond L. Barnhill, Helmut Beltraminelli et al. "Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts." European Journal of Cancer 156 (2021): 202-216.
“Skin Cancer: Skin Cancer Facts: Common Skin Cancer Types.” American Cancer Society. Accessed June 7, 2022. https://www.cancer.org/cancer/skin-cancer.html/.
Kassem, Mohamed & Hosny, Khalid & Fouad, M.. (2020). Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.3003890.
Brinker TJ, Hekler A, Utikal JS, Grabe N, Schadendorf D, Klode J, Berking C, Steeb T, Enk AH, von Kalle C. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review. J Med
Internet Res. 2018 Oct 17;20(10):e11936. doi: 10.2196/11936. PMID: 30333097; PMCID: PMC6231861.
Gessert, N., Nielsen, M., Shaikh, M., Werner, R., & Schlaefer, A. (2020). Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX, 7, 100864.
Rezvantalab, A., Safigholi, H., & Karimijeshni, S. (2018). Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. arXiv preprint arXiv:1810.10348.
Tschandl, P., Codella, N., Akay, B. N., Argenziano, G., Braun, R. P., Cabo, H., ... & Kittler, H. (2019). Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. The lancet oncology, 20(7), 938-947.
Hosny, K. M., Kassem, M. A., & Foaud, M. M. (2019). Classification of skin lesions using transfer learning and augmentation with Alex-net. PloS one, 14(5), e0217293.
Hekler, A., Kather, J. N., Krieghoff-Henning, E., Utikal, J. S., Meier, F., Gellrich, F. F., ... & Brinker, T. J. (2020). Effects of label noise on deep learning-based skin cancer classification. Frontiers in Medicine, 7, 177.
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
Sucholutsky, I., & Schonlau, M. (2020). Secdd: Efficient and secure method for remotely training neural networks. arXiv preprint arXiv:2009.09155.
Codella, N. C., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., ... & Halpern, A. (2018, April). 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 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 168-172). IEEE.
Tschandl, P. et al. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5:180161 doi: 10.1038/sdata.2018.161 (2018)
What is a convolutional neural network? What is a Convolutional Neural Network? - MATLAB & Simulink. (n.d.). Retrieved June 14, 2022, from https://in.mathworks.com/discovery/convolutional-neural-network-atlab.html?s_tid=srchtitle_Convolutional+neural+network_1
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
