Image Segmentation Using Hybrid Clustering Algorithms for Machine Learning-Based Skin Cancer Identification

Riza Maulana, Diva Cahaya Interiesta, Annisa Kurnia Sofy, Ilham Habib Maulana, Amir Saleh

Abstract


Early identification of skin cancer is crucial to increasing the chances of a cure and reducing mortality rates. This research aims to develop a method for identifying skin cancer using image processing techniques, specifically the hybrid clustering method. This method integrates machine learning with fuzzy c-means clustering (FCM) and hierarchical clustering (HC) segmentation techniques to segment skin cancer more accurately. Hybrid clustering is used to separate suspicious areas in skin images, resulting in more precise segmentation compared to conventional methods. The segmentation results are then used as input for various machine learning methods that are trained to recognize patterns in identifying types of skin cancer. Tests were carried out using data obtained from the Kaggle Dataset, and the results showed that the proposed method was able to achieve a high level of accuracy in identifying skin cancer. After segmentation, the ensemble learning method yielded the best identification results. The Random Forest algorithm, which is applied to process and analyze features from skin images, shows higher performance compared to other machine learning methods. Tests show that the Random Forest method with the proposed segmentation achieves an accuracy level of up to 89%, while other machine learning methods such as K-Nearest Neighbor only achieve an accuracy level of around 86%. This research makes an important contribution to the development of efficient and reliable diagnostic tools for skin cancer identification, with appropriate segmentation methods proven to increase accuracy.

Full Text:

PDF

References


Ahammed, M., Mamun, M. Al, & Uddin, M. S. (2022). A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics, 2(April), 100122. https://doi.org/10.1016/j.health.2022.100122

Anggraeny, F. T., Rahmat, B., & Pratama, S. P. (2020). Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 15(2), 114. https://doi.org/10.30872/jim.v15i2.4648

Burada, S., Manjunathswamy, B. E., & Kumar, M. S. (2024). Measurement : Sensors Early detection of melanoma skin cancer : A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional. Measurement: Sensors, 33(December 2023), 101168. https://doi.org/10.1016/j.measen.2024.101168

Carcagnì, P., Del Coco, M., Leo, M., & Distante, C. (2015). Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus, 4(1). https://doi.org/10.1186/s40064-015-1427-3

Cheon, M.-K., Lee, W.-J., Hyun, C.-H., & Park, M. (2011). Rotation Invariant Histogram of Oriented Gradients. International Journal of Fuzzy Logic and Intelligent Systems, 11(4), 293–298. https://doi.org/10.5391/ijfis.2011.11.4.293

Dhal, K. G., Das, A., Sasmal, B., Ray, S., Rai, R., & Garai, A. (2023). Fuzzy C-Means for image segmentation: challenges and solutions. Multimedia Tools and Applications, 83, 1–37. https://doi.org/10.1007/s11042-023-16569-2

Elngar, A. A., Kumar, R., Hayat, A., & Churi, P. (2021). Intelligent System for Skin Disease Prediction using Machine Learning. Journal of Physics: Conference Series, 1998(1). https://doi.org/10.1088/1742-6596/1998/1/012037

Fariza, A., Arifin, A. Z., Astuti, E. R., & Kurita, T. (2019). Segmenting tooth components in dental X-ray images using Gaussian kernel- based conditional spatial Fuzzy C-Means clustering algorithm. International Journal of Intelligent Engineering and Systems, 12(3). https://doi.org/10.22266/IJIES2019.0630.12

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(August), 104065. https://doi.org/10.1016/j.compbiomed.2020.104065

Hoshyar, A. N., Al-Jumaily, A., & Hoshyar, A. N. (2014). The beneficial techniques in preprocessing step of skin cancer detection system comparing. Procedia Computer Science, 42(C), 25–31. https://doi.org/10.1016/j.procs.2014.11.029

Islam, A. R., Alammari, A., & Buckles, B. (2024). Human skin detection: An unsupervised machine learning way. Journal of Visual Communication and Image Representation, 98(January 2022), 104046. https://doi.org/10.1016/j.jvcir.2024.104046

Islami, F. (2021). Implementation of HSV- based Thresholding Method for Iris Detection. Journal of Computer Networks, Architecture, and High-Performance Computing, 3(1), 98–104. https://doi.org/10.47709/cnahpc.v3i1.939

Kurniastuti, I., Yuliati, E. N. I., Yudianto, F., & Wulan, T. D. (2022). Determination of Hue Saturation Value (HSV) color feature in kidney histology image. Journal of Physics: Conference Series, 2157(1). https://doi.org/10.1088/1742-6596/2157/1/012020

Mamun, Md. Al, & Uddin, M. S. (2021). Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study. Journal of Computer and Communications, 09(04), 67–84. https://doi.org/10.4236/jcc.2021.94005

Masood, A., & Al-Jumaily, A. A. (2013). Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions. Journal of Signal and Information Processing, 04(03), 66–71. https://doi.org/10.4236/jsip.2013.43b012

Nanni, L., Loreggia, A., Lumini, A., & Dorizza, A. (2023). A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies. Journal of Imaging, 9(2). https://doi.org/10.3390/jimaging9020035

Saber Iraji, M., & Tosinia, A. (2012). Skin Color Segmentation in YCBCR Color Space with Adaptive Fuzzy Neural Network (Anfis). International Journal of Image, Graphics and Signal Processing, 4(4), 35–41. https://doi.org/10.5815/ijigsp.2012.04.05

Sharma, S., & Chaudhary, P. (2023). Machine learning and deep learning. Quantum Computing and Artificial Intelligence: Training Machine and Deep Learning Algorithms on Quantum Computers, 71–84. https://doi.org/10.1515/9783110791402-004

Singh, S. K., Abolghasemi, V., & Anisi, M. H. (2023). Fuzzy Logic with Deep Learning for Detection of Skin Cancer. Applied Sciences, 124–134. https://doi.org/10.4324/9781315232140-14

Tahyudin, G. G., Sulistiyo, M. D., Arzaki, M., & Rachmawati, E. (2024). Classifying Gender Based on Face Images Using Vision Transformer. JOIV : International Journal on Informatics Visualization, 8(1), 18. https://doi.org/10.62527/joiv.8.1.1923




DOI: https://doi.org/10.30596/jcositte.v5i2.21016

Refbacks

  • There are currently no refbacks.