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Exploring the application of machine learning for skin cancer image identification
- 1 New Channel Jinan Jinqiu A-Level College
* Author to whom correspondence should be addressed.
Abstract
Cancer encompasses a broad spectrum of diseases marked by abnormal cell growth that can potentially spread throughout the body. Common types include breast, lung, colon, rectal, and skin cancers, among others. Early detection is crucial for effective treatment of skin cancer, which ranks among the prevalent forms of cancer. However, traditional diagnostic methods are time-consuming and depend on the expertise of dermatologists. This research aims to investigate the application of machine learning (ML) for identifying skin cancer from images, aiming to improve early detection and diagnosis. Various image preprocessing techniques, feature extraction methods, and ML algorithms are applied to a dataset of skin lesion images. Various machine learning models are assessed and compared based on relevant metrics for their effectiveness in detecting skin cancer. Preliminary findings indicate that ML algorithms can achieve high accuracy in skin cancer identification, potentially improving diagnostic efficiency and accessibility. However, the research also highlights obstacles such as imbalanced data and the need for model interpretability, which must be addressed for practical implementation. This research contributes to the expanding knowledge on ML applications in healthcare, particularly in dermatology. It highlights the potential of ML in skin cancer identification and provides insights into the challenges and limitations that need to be overcome for successful implementation. The study emphasizes the necessity for future research to refine these techniques and enhance their clinical applicability.
Keywords
skin cancer, machine learning, image identification
[1]. U.S. Department of Health and Human Services. (2014). The Surgeon General’s call to action to prevent skin cancer. U.S. Dept of Health and Human Services, Office of the Surgeon General. Available at http://www.surgeongeneral.gov
[2]. Teuwen, J., & Moriakov, N. (2020). Chapter 20 - Convolutional neural networks. In S. K. Zhou, D. Rueckert, & G. Fichtinger (Eds.), Handbook of medical image computing and computer-assisted intervention (pp. 481-501). Academic Press. doi:10.1016/B978-0-12-816176-0.00025-9
[3]. Erciyes, K. (2019). Introduction to real-time systems. In Distributed real-time systems. Springer, Cham. doi:10.1007/978-3-030-22570-4_1
[4]. World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. Geneva. e-ISBN: 978-92-4-002920-0. Available at https://iris.who.int/bitstream/handle/10665/341996/9789240029200-eng.pdf?sequence=1
[5]. Apalla, Z., Nashan, D., Weller, R. B., & others. (2017). Skin cancer: Epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches. Dermatology and Therapy (Heidelberg), 7(Suppl 1), 5–19. doi:10.1007/s13555-016-0165-y
[6]. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2014). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. doi:10.1016/j.csbj.2014.11.005
[7]. Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018). When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2811-2821. doi:10.1109/TGRS.2017.2783902
[8]. Naik, P. P., & Desai, M. B. (2022). Basal cell carcinoma: A narrative review on contemporary diagnosis and management. Oncology and Therapy, 10, 317-335. doi:10.1007/s40487-022-00201-8
[9]. Cameron, M. C., Lee, E., Hibler, B. P., Barker, C. A., Mori, S., Cordova, M., Nehal, K. S., & Rossi, A. M. (2019). Basal cell carcinoma: Epidemiology, pathophysiology, clinical and histological subtypes, and disease associations. Journal of the American Academy of Dermatology, 80(2), 303-317. doi:10.1016/j.jaad.2018.03.060
[10]. Tanese, K. (2019). Diagnosis and management of basal cell carcinoma. Current Treatment Options in Oncology, 20(2), 13. doi:10.1007/s11864-019-0610-0
[11]. Ferrante di Ruffano, L., Dinnes, J., Chuchu, N., Bayliss, S. E., Takwoingi, Y., Davenport, C., Matin, R. N., O'Sullivan, C., Roskell, D., Deeks, J. J., Williams, H. C., & Cochrane Skin Cancer Diagnostic Test Accuracy Group. (2018). Exfoliative cytology for diagnosing basal cell carcinoma and other skin cancers in adults. The Cochrane Database of Systematic Reviews, 12(12), CD013187. doi:10.1002/14651858.CD013187
[12]. Yanofsky, V. R., Mercer, S. E., & Phelps, R. G. (2011). Histopathological variants of cutaneous squamous cell carcinoma: A review. Journal of Skin Cancer, 2011, 210813. doi:10.1155/2011/210813
[13]. Combalia, A., & Carrera, C. (2020). Squamous cell carcinoma: An update on diagnosis and treatment. Dermatology Practical & Conceptual, 10(3), e2020066. doi:10.5826/dpc.1003a66
[14]. Tan, G., Liu, Y., & Sridevi, S. (2023). Computer vision on image recognition technology. In B. J. Jansen, Q. Zhou, & J. Ye (Eds.), Proceedings of the 2nd International Conference on Cognitive Based Information Processing and Applications (CIPA 2022) (pp. 994-999). Springer. doi:10.1007/978-981-19-9373-2_6
[15]. Andreopoulos, A., & Tsotsos, J. K. (2013). 50 years of object recognition: Directions forward. Computer Vision and Image Understanding, 117(8), 827-891. doi:10.1016/j.cviu.2013.04.005
[16]. Vaz, J. M., & Balaji, S. (2021). Convolutional neural networks (CNNs): Concepts and applications in pharmacogenomics. Molecular Diversity, 25(3), 1569–1584. doi:10.1007/s11030-021-10225-3
[17]. Hentschel, C., Wiradarma, T. P., & Sack, H. (2016). Fine tuning CNNs with scarce training data: Adapting imagenet to art epoch classification. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3693-3697). Phoenix, AZ, USA. doi:10.1109/ICIP.2016.7533049
[18]. Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., Han, W., Huang, M., Jin, Q., Lan, Y., Liu, Y., Liu, Z., Lu, Z., Qiu, X., Song, R., Tang, J., Yuan, J., Zhao, W. X., & Zhu, J. (2021). Pre-trained models: Past, present and future. AI Open, 2, 225-250. doi:10.1016/j.aiopen.2021.08.002
[19]. Shaha, M., & Pawar, M. (2018). Transfer learning for image classification. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 656-660). Coimbatore, India. doi:10.1109/ICECA.2018.8474802
[20]. Li, Y. (2022). Research and application of deep learning in image recognition. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) (pp. 994-999). Shenyang, China. doi:10.1109/ICPECA53709.2022.9718847
[21]. Li, L. (2020). Application of deep learning in image recognition. Journal of Physics: Conference Series, 1693, 012128. https://doi.org/10.1088/1742-6596/1693/1/012128
[22]. Qureshi, A. S., & Roos, T. (2021). Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data. arXiv Preprint arXiv:2103.12068. https://doi.org/10.48550/arXiv.2103.12068
[23]. Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in Biology and Medicine, 127, 104065. https://doi.org/10.1016/j.compbiomed.2020.104065
[24]. Esteva, A., Kuprel, B., Novoa, R., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118. https://doi.org/10.1038/nature21056
[25]. Liu, C. F., Hsu, J., Xu, X., et al. (2021). Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke. Communications Medicine, 1, 61. https://doi.org/10.1038/s43856-021-00062-8
[26]. Goyal, M., Oakley, A., Bansal, P., Dancey, D., & Yap, M. H. (2019). Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access, 8, 4171-4181. https://doi.org/10.1109/ACCESS.2019.2960504
[27]. Das, K., Cockerell, C. J., Patil, A., Pietkiewicz, P., Giulini, M., Grabbe, S., & Goldust, M. (2021). Machine learning and its application in skin cancer. International Journal of Environmental Research and Public Health, 18(24), 13409. https://doi.org/10.3390/ijerph182413409
[28]. Guo, P., Luo, Y., Mai, G., Zhang, M., Wang, G., Zhao, M., Gao, L., Li, F., & Zhou, F. (2014). Gene expression profile-based classification models of psoriasis. Genomics, 103(1), 48–55. https://doi.org/10.1016/j.ygeno.2013.11.001
[29]. Abhishek, K., Kawahara, J., & Hamarneh, G. (2021). Predicting the clinical management of skin lesions using deep learning. Scientific Reports, 11, 7769. https://doi.org/10.1038/s41598-021-87064-7
[30]. Chan, S., Reddy, V., Myers, B., Thibodeaux, Q., Brownstone, N., & Liao, W. (2020). Machine learning in dermatology: Current applications, opportunities, and limitations. Dermatology and Therapy, 10(3), 365–386. https://doi.org/10.1007/s13555-020-00372-0
[31]. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248-255). Miami, FL.
[32]. Mendonça, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013). PH2 – A dermoscopic image database for research and benchmarking. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5437-5440). Osaka.
[33]. Tschandl, P., Rosendahl, C., & Kittler, H. (2018). The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, 180161. https://doi.org/10.1038/sdata.2018.161
[34]. Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Wong, K., Avraham, T., & Haedersdal, M. (2021). A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Scientific Data, 8, 34. https://doi.org/10.1038/s41597-021-00823-7
[35]. Shetty, B., Fernandes, R., Rodrigues, A. P., et al. (2022). Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports, 12, 18134. https://doi.org/10.1038/s41598-022-22644-9
[36]. Salamaa, W. M., & Aly, M. H. (2021). Deep learning design for benign and malignant classification of skin lesions: A new approach. Multimedia Tools and Applications, 80, 26795-26811. https://doi.org/10.1007/s11042-021-11000-0
[37]. Martin-Barragan, B., Lillo, R., & Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1), 146-155. https://doi.org/10.1016/j.ejor.2012.08.017
[38]. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. https://doi.org/10.1016/j.neucom.2019.10.118
[39]. Sheth, V., Tripathi, U., & Sharma, A. (2022). A comparative analysis of machine learning algorithms for classification purpose. Procedia Computer Science, 215, 422-431. https://doi.org/10.1016/j.procs.2022.12.044
[40]. Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin cancer detection: A review using deep learning techniques. International Journal of Environmental Research and Public Health, 18(10), 5479. https://doi.org/10.3390/ijerph18105479
[41]. Juan, C. K., Su, Y. H., Wu, C. Y., et al. (2023). Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation. Scientific Reports, 13, 17087. https://doi.org/10.1038/s41598-023-42693-y
[42]. Fenza, G., Gallo, M., Loia, V., Orciuoli, F., & Herrera-Viedma, E. (2021). Data set quality in machine learning: Consistency measure based on group decision making. Applied Soft Computing, 106, 107366. https://doi.org/10.1016/j.asoc.2021.107366
[43]. Rezk, E., Eltorki, M., & El-Dakhakhni, W. (2023). Interpretable skin cancer classification based on incremental domain knowledge learning. Journal of Healthcare Informatics Research, 7, 59–83. https://doi.org/10.1007/s41666-023-00127-4
[44]. Avanija, J., Mohan Reddy, C. C., Chandan Reddy, C. S., Harshavardhan Reddy, D., Narasimhulu, T., & Hardhik, N. V. (2023). Skin cancer detection using ensemble learning. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 184–189). https://doi.org/10.1109/ICSCSS57650.2023.10169747
[45]. Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2), 757–774. https://doi.org/10.1016/j.jksuci.2023.01.014
[46]. Zhang, X., Wang, S., Liu, J., et al. (2018). Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Medical Informatics and Decision Making, 18(Suppl. 2), 59. https://doi.org/10.1186/s12911-018-0631-9
[47]. He, X., Wang, Y., Zhao, S., & Chen, X. (2023). Co-attention fusion network for multimodal skin cancer diagnosis. Pattern Recognition, 133, 108990. https://doi.org/10.1016/j.patcog.2022.108990
[48]. Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538, 20–23. https://doi.org/10.1038/538020
[49]. Rosemann, A., & Zhang, X. (2022). Exploring the social, ethical, legal, and responsibility dimensions of artificial intelligence for health – a new column in intelligent medicine. Intelligent Medicine, 2(2), 103–109. https://doi.org/10.1016/j.imed.2021.12.002
[50]. Bischl, B., Binder, M., Lang, M., et al. (2021). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(1), e1402.
[51]. Li, L., Zhang, Q., Ding, Y., Jiang, H., Thiers, B. H., & Wang, J. Z. (2014). Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system. BMC Medical Imaging, 14(1), 36. https://doi.org/10.1186/1471-2342-14-36
[52]. Wen, D., Khan, S. M., Xu, A. J., et al. (2022). Characteristics of publicly available skin cancer image datasets: A systematic review. The Lancet Digital Health, 4(1), e64–e74. https://doi.org/10.1016/S2589-7500(21)00252-1
[53]. World Health Organization. (2020). PRESS RELEASE No. 292. Retrieved June 27, 2023, from https://www.iarc.who.int/wp-content/uploads/2020/12/pr292_E.pdf
[54]. ONCOCARE (2023). Skin cancer - OncoCare Cancer Centre. Retrieved from https://oncocare.sg/zh-hans/%E7%9A%AE%E8%82%A4%E7%99%8C/
[55]. American Cancer Society. (2023). Basal and squamous cell skin cancer. Retrieved August 6, 2023, from https://www.cancer.org/cancer/types/basal-and-squamous-cell-skin-cancer.html
[56]. American Cancer Society. (2023). What is melanoma skin cancer. Retrieved August 6, 2023, from https://www.cancer.org/cancer/types/melanoma-skin-cancer/about/what-is-melanoma.html
[57]. American Cancer Society. (2023). Treating melanoma skin cancer. Retrieved August 6, 2023, from https://www.cancer.org/cancer/types/melanoma-skin-cancer/treating.html
[58]. Ruder, S. (2017). Transfer learning - Machine learning's next frontier. Retrieved July 16, 2023, from http://ruder.io/transfer-learning/
[59]. Brownlee, J. (2019). Overfitting and underfitting with machine learning algorithms. Machine Learning Mastery. Retrieved June 15, 2023, from https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
[60]. Zumel, N. (2022). Real-time data in machine learning: Challenges and solutions. DataVersity. Retrieved from https://www.dataversity.net/real-time-data-in-machine-learning-challenges-and-solutions/
[61]. Gordon, R. (2022). Seeing into the future: Personalized cancer screening with artificial intelligence. MIT CSAIL. Retrieved June 12, 2023, from https://news.mit.edu/2022/seeing-future-personalized-cancer-screening-artificial-intelligence-0121
Cite this article
Chen,S. (2024). Exploring the application of machine learning for skin cancer image identification. Advances in Engineering Innovation,11,1-14.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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