Accurate Detection of Basal Cell Carcinoma Using Fuzzy U-Net and Deep Learning on Dermoscopic Images
DOI:
https://doi.org/10.22399/ijcesen.2475Keywords:
BCC, wiener filter, fuzzy U-Net segmentation, DNNAbstract
Basal cell carcinoma (BCC) is a common kind of skin cancer that is distinguished by the presence of telangiectasias, which are tiny blood veins that resemble trees and are frequently seen inside skin lesions. Precise recognition of these characteristics is essential for prompt diagnosis and successful therapy. Deep learning (DL) models have advanced in skin cancer imaging in recent years, increasing the accuracy of diagnosis and feature segmentation. The ISIC 2019 dataset, a comprehensive collection of dermoscopic pictures covering a variety of skin lesions, including BCC, was employed in our suggested approach. Our approach started with applying a Wiener filter to denoise the images. This preprocessing step significantly improved image quality, making critical features more discernible and facilitating subsequent analysis. After denoising, we implemented the Fuzzy U-Net model for image segmentation. This model excels at accurately delineating lesions, providing precise boundaries that are essential for effective classification. A deep neural network (DNN) was then trained using the segmented pictures, enabling it to differentiate basal cell carcinoma from other skin diseases by identifying important characteristics. We used common assessment measures including precision of 97%, F1 score of 98%, and AUC of 0.99% and testing accuracy of 97.47% to assess our model's performance. The outcomes show that our method is reliable and successful, with a high level of accuracy in detecting basal cell cancer. In addition to expediting the diagnostic procedure, this approach may enhance patient outcomes by enabling earlier discovery and treatment.
References
[1] Maurya, A., Stanley, R. J., Lama, N., Nambisan, A. K., Patel, G., Saeed, D., et al. (2024). Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis. Journal of Imaging Informatics in Medicine. 37; 92-106. https://doi.org/10.1007/s10278-023-00924-8
[2] Dhivyaa, C. R., Sangeetha, K., Balamurugan, M., Amaran, S., Vetriselvi, T., & Johnpaul, P. (2020). Skin lesion classification using decision trees and random forest algorithms. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02675-8
[3] Fried, L., Tan, A., Bajaj, S., Liebman, T. N., Polsky, D., & Stein, J. A. (2020). Technological advances for the detection of melanoma. Advances in diagnostic techniques. Journal of the American Academy of Dermatology. 83; 983-992. https://doi.org/10.1016/j.jaad.2020.03.122
[4] Birkenfeld, J. S., Tucker-Schwartz, J. M., Soenksen, L. R., Aviles-Izquierdo, J. A., & Marti-Fuster, B. (2020). Computer-aided classification of suspicious pigmented lesions using wide-field images. Computer Methods and Programs in Biomedicine. 195. https://doi.org/10.1016/j.cmpb.2020.105631
[5] Abbas, S., Jalil, Z., Javed, A. R., Batool, I., Khan, M. Z., Noorwali, A., et al. (2021). BCD-WERT: A novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm. PeerJ Computer Science. 7. https://doi.org/10.7717/peerj-cs.390
[6] Gadamsetty, S., Ch, R., Ch, A., Iwendi, C., & Gadekallu, T. R. (2022). Hash-based deep learning approach for remote sensing satellite imagery detection. Water. 14. https://doi.org/10.3390/w14050707
[7] Campanella, G., Navarrete-Dechent, C., Liopyris, K., Monnier, J., Aleissa, S., Minhas, B., et al. (2022). Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy. Journal of Investigative Dermatology. 142; 97-103. https://doi.org/10.1016/j.jid.2021.06.015
[8] Ahammed, M., Mamun, M. A., & Uddin, M. S. (2022). A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics. 2. https://doi.org/10.1016/j.health.2022.100122
[9] Kimeswenger, S., Tschandl, P., Noack, P., Hofmarcher, M., Rumetshofer, E., Kindermann, H., et al. (2021). Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns. Modern Pathology. 34; 895-903. https://doi.org/10.1038/s41379-020-00712-7
[10] Sendin-Martin, M., Lara-Caro, M., Harris, U., Moronta, M., Rossi, A., Lee, E., et al. (2022). Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm. Journal of Investigative Dermatology. 142; 1291-1299. https://doi.org/10.1016/j.jid.2021.09.029
[11] Chen, M., Feng, X., Fox, M. C., Reichenberg, J. S., Lopes, F. C. P. S., Sebastian, K. R., et al. (2022). Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma. Journal of Biomedical Optics. 27(6). https://doi.org/10.1117/1.jbo.27.6.065004
[12] Brorsen, L. F., McKenzie, J. S., Pinto, F. E., Glud, M., Hansen, H. S., Haedersdal, M., et al. (2024). Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning. Experimental Dermatology. 33. https://doi.org/10.1111/exd.15141
[13] Serrano, C., Laz, M., Serrano, A., Toledo-Pastrana, T., Barros-Tornay, R., & Acha, B. (2022). Clinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma. Journal of Imaging. 8. https://doi.org/10.3390/jimaging8070197
[14] Dragomir, A. C., Cocuz, I. G., Cotoi, O. S., & Azamfirei, L. (2022). Artificial intelligence-based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma. Acta Marisiensis-Seria Medica. 68(4). https://doi.org/10.2478/amma-2022-0020
[15] Courtenay, L. A., González-Aguilera, D., Lagüela, S., Del Pozo, S., Ruiz, C., Barbero-García, I., et al. (2022). Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures. Journal of Clinical Medicine. 11(9). https://doi.org/10.3390/jcm11092315
[16] Surkov, Y. I., Serebryakova, I. A., Kuzinova, Y. K., Konopatskova, O. M., Safronov, D. V., Kapralov, S. V., et al. (2024). Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo. Diagnostics. 14. https://doi.org/10.3390/diagnostics14020202
[17] Jones, O. T., Matin, R. N., van der Schaar, M., Bhayankaram, K. P., Ranmuthu, C. K. I., Islam, M. S., et al. (2022). Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. The Lancet Digital Health, 4(6), e466–e476. https://doi.org/10.1016/s2589-7500(22)00023-1
[18] Alkhushayni, S., Al-zaleq, D., Andradi, L., & Flynn, P. (2022). The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas. Journal of Skin Cancer. 2022. https://doi.org/10.1155/2022/2839162
[19] Raut, R., Borole, Y., Patil, S., Khan, V., & Takale, D. G. (2022). Skin Disease Classification Using Machine Learning Algorithms. NeuroQuantology. 20(10); 9624-9629. https://doi.org/10.48047/nq.2019.17.03.2011
[20] Shavlokhova, V., Vollmer, M., Gholam, P., Saravi, B., Vollmer, A., Hoffmann, J., et al. (2022). Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data. Journal of Personalized Medicine. 12(9). https://doi.org/10.3390/jpm12091471
[21] Hurtado, J., & Reales, F. (2021). A machine learning approach for the recognition of melanoma skin cancer on macroscopic images. TELKOMNIKA Telecommunication, Computing, Electronics and Control. 19(4); 1357-1368. https://doi.org/10.12928/telkomnika.v19i4.20292
[22] Lan, X., Guo, G., Wang, X., Yan, Q., Xue, R., Li, Y., et al. (2024). Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features. Skin Research and Technology. 30. https://doi.org/10.1111/srt.13571
[23] Ali, S. R., Strafford, H., Dobbs, T. D., Fonferko-Shadrach, B., Lacey, A. S., Pickrell, W. O., et al. (2022). Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing. Frontiers in Surgery. 9. https://doi.org/10.3389/fsurg.2022.870494
[24] Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K. (2022). Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports. 12. https://doi.org/10.1038/s41598-022-22644-9
[25] Yacob, F., Siarov, J., Villiamsson, K., Suvilehto, J. T., Sjöblom, L., Kjellberg, M., et al. (2023). Weakly supervised detection and classification of basal cell carcinoma using graph transformer on whole slide images. Scientific Reports. 13. https://doi.org/10.1038/s41598-023-33863-z
[26] Liu, L., Qi, M., Li, Y., Liu, Y., Liu, X., Zhang, Z., et al. (2022). Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning. Biosensors. 12(10). https://doi.org/10.3390/bios12100790
[27] Wako, B. D., Dese, K., Ulfata, R. E., Nigatu, T. A., Turunbedu, S. K., & Kwa, T. (2022). Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning. Cancer Control. 29; 1-16.
[28] Hatem, M. Q. (2022). Skin lesion classification system using a K-nearest neighbor algorithm. Visual Computing for Industry, Biomedicine, and Art. 5(7). https://doi.org/10.1186/s42492-022-00103-6
[29] Thanka, M. R., Edwin, E. B., Ebenezer, V., Sagayam, K. M., Reddy, B. J., Günerhan, H., et al. (2023). A hybrid approach for melanoma classification using ensemble machine learning techniques with deep transfer learning. Computer Methods and Programs in Biomedicine Update. 3. https://doi.org/10.1016/j.cmpbup.2023.100103
[30] Luu, N. T., Le, T. H., Phan, Q. H., & Pham, T. T. H. (2021). Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm. Journal of Biomedical Optics. 26(7). https://doi.org/10.1117/1.jbo.26.7.075001
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
