An Efficient Approach for Automatic Melanoma Detection Based on Data Balance and Deep Neural Network | ||
| Journal of Computing and Communication | ||
| Article 2, Volume 3, Issue 1, January 2024, Pages 22-32 PDF (1.04 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jocc.2024.339917 | ||
| Authors | ||
| Metwally Rashad* 1; Mahmoud mansour2; Mohamed Taha3 | ||
| 1Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt | ||
| 2Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University | ||
| 3Computer Science department, Faculty of computers and Informatics, Benha University | ||
| Abstract | ||
| One of the most serious types of skin cancer is Melanoma, which can be fatal if it is not detected in its early stages. Patients need to visit a dermatologist to diagnose infected skin and determine if it is Melanoma or not. The traditional method for a dermatologist is more complicated and requires extensive experience to look at the skin with a dermatoscope and then provide a biopsy report for diagnosis. Instead of traditional methods, artificial intelligence, especially deep learning, provides powerful results in experience-based problems without the need for experts in the specific field of the problem. For this reason, deep neural network architectures can be useful for dermatologists and patients in the early stages of identifying melanoma skin cancer. This paper offers a proposed approach for automatically classifying Melanoma using convolution neural network (CNN) architectures VGG19 and GoogleNet. From data balance for input images, which makes a huge difference in results to preprocessing images and testing VGG19, GoogleNet in the feature extraction process and final binary classification with class 1 means Melanoma and class 0 means nonmelanoma. A dataset was used from the international skin imaging collaboration datastores (ISIC 2019) with 7146 total used images. Proposed approach results show that GoogleNet accuracy is 80.07 % and 81.28% in the training and testing dataset, and VGG19 accuracy is 85.57 % and 78.21 % in the training and testing dataset. | ||
| Keywords | ||
| melanoma; dermatologist; dermatoscope; deep learning; CNN | ||
| References | ||
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[1] American Academy of Dermatology Association. Types of skin cancer and early diagnosis effect. Available online: https://www.aad.org/public/diseases/skin-cancer/types/common (accessed on 5 August 2023)
[2] World Cancer Research Fund International. Number of diagnosed Melanoma and nonmelanoma worldwide. Available online: https://www.wcrf.org/cancer-trends/skin-cancer-statistics/ (accessed on 5 August 2023)
[3] The American Cancer Society. Melanoma estimation numbers in US for 2023. Available online : https://www.cancer.org/cancer/types/melanoma-skin-cancer/about/key-statistics.html (accessed on 5 August 2023)
[4] World Health Organization. Number of global cases of Melanoma and nonmelanoma. Available online:https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer (accessed on 5 August 2023)
[5] American Academy of Dermatology Association. ABCDE rule of identifying Melanoma. Available online: https://www.aad.org/public/diseases/skin-cancer/find/at-risk/abcdes (accessed on 5 August 2023)
[6] 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.
[7] Bhatt, H., Shah, V., Shah, K., Shah, R., & Shah, M. (2022). State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: A comprehensive review. Intelligent Medicine.
[8] Mishra, N. K., & Celebi, M. E. (2016). An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning. ArXiv. /abs/1601.07843
[9] N. Hameed, A. Ruskin, K. Abu Hassan and M. A. Hossain, "A comprehensive survey on image-based computer aided diagnosis systems for skin cancer," 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Chengdu, China, 2016, pp. 205-214
[10] Popescu, D., & Ichim, L. (2022). New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors, 22(2), 496.
[11] Z. E. Diame, M. N. Al-Berry, M. A. . -M. Salem and M. Roushdy, "Deep Learning Architiectures For Aided Melanoma Skin Disease Recognition: A Review," 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), Cairo, Egypt, 2021, pp. 324-329.
[12] Adegun, A., Viriri, S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev 54, 811–841 (2021).
[13] Hosseinzadeh Kassani, S., & Hosseinzadeh Kassani, P. (2019). A comparative study of deep learning architectures on melanoma detection. Tissue and Cell, 58, 76-83.
[14] Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., von Kalle, C., Fröhling, S., & Brinker, T. J. (2019). Pathologist-level classification of histopathological melanoma images with deep neural networks. European Journal of Cancer, 115, 79-83.
[15] Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., von Kalle, C., Fröhling, S., Schilling, B., & Utikal, J. S. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119, 11-17.
[16] Bisla, D., Choromanska, A., Stein, J. A., Polsky, D., & Berman, R. (2019). Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation. ArXiv.
[17] Mijwil, M.M. Skin cancer disease images classification using deep learning solutions. Multimed Tools Appl 80, 26255–26271 (2021).
[18] Aljohani, K., & Turki, T. (2022). Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks. AI, 3(2), 512-525.
[19] Fraiwan, M., & Faouri, E. (2022). On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning. Sensors, 22(13), 4963.
[20] S. Kavitha, R. Shalini, N. Harini Sree and J. Akash, "Intelligant Segmentation and Classification for Skin Cancer Prediction," 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2023, pp. 1-6
[21] Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021).
[22] H. -C. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285-1298, May 2016.
[23] International Skin Imaging Collaboration. Description of ISIC 2019 challenge. Available online: https://challenge.isic-archive.com/landing/2019/ (accessed on 1 August 2023)
[24] International Skin Imaging Collaboration. Dataset source for download. Available online: https://api.isic-archive.com/collections/?pinned=true (accessed on 1 August 2023). | ||
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