COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION

A. Muh. Fitrah  Asfar; Mardiyyah Hasnawi; Herdianti Darwis

doi:10.52436/1.jutif.2024.5.6.2514

A. Muh. Fitrah Asfar Informatics engineering, Computer science, Universitas Muslim Indonesia, Indonesia
Mardiyyah Hasnawi Informatics engineering, Computer science, Universitas Muslim Indonesia, Indonesia
Herdianti Darwis Informatics engineering, Computer science, Universitas Muslim Indonesia, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.6.2514

Keywords: Feature Extraction, Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, Local Binary Patterns, Skin Cancer

Abstract

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

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References

H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.

S. Gondhowiardjo et al., “Five-Year Cancer Epidemiology at the National Referral Hospital: Hospital-Based Cancer Registry Data in Indonesia,” JCO Global Oncol, vol. 7, pp. 190–203, 2021, doi: 10.1200/GO.20.

R. N. P. Fauziyyah, M. Komariah, and Y. K. Herliani, “Sunlight Exposure and Protection Behavior as Prevention of Skin Cancer in Nursing Students,” Indonesian Journal of Cancer, vol. 17, no. 1, p. 1, Mar. 2023, doi: 10.33371/ijoc.v17i1.921.

Y. Jusman, I. M. Firdiantika, D. A. Dharmawan, and K. Purwanto, “Performance of multi layer perceptron and deep neural networks in skin cancer classification,” in LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 534–538. doi: 10.1109/LifeTech52111.2021.9391876.

N. Zhang, Y. X. Cai, Y. Y. Wang, Y. T. Tian, X. L. Wang, and B. Badami, “Skin cancer diagnosis based on optimized convolutional neural network,” Artif Intell Med, vol. 102, Jan. 2020, doi: 10.1016/j.artmed.2019.101756.

K. V. Laikova et al., “Advances in the understanding of skin cancer: Ultraviolet radiation, mutations, and antisense oligonucleotides as anticancer drugs,” Apr. 17, 2019, MDPI AG. doi: 10.3390/molecules24081516.

K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Classification of skin lesions using transfer learning and augmentation with Alex-net,” PLoS One, vol. 14, no. 5, May 2019, doi: 10.1371/journal.pone.0217293.

M. Selvarasa and A. Aponso, “A Critical Analysis of Computer Aided Approaches for Skin Cancer Screening,” in Proceedings of International Conference on Image Processing and Robotics, ICIPRoB 2020, Institute of Electrical and Electronics Engineers Inc., Mar. 2020. doi: 10.1109/ICIP48927.2020.9367370.

K. Thurnhofer-Hemsi and E. Domínguez, “A Convolutional Neural Network Framework for Accurate Skin Cancer Detection,” Neural Process Lett, vol. 53, no. 5, pp. 3073–3093, Oct. 2021, doi: 10.1007/s11063-020-10364-y.

M. A. Sabri and Institute of Electrical and Electronics Engineers, 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) : June 09-11, 2020, Faculty of Sciences Dhar El Mahraz (FSDM), Fez, Morocco.

A. Javaid, M. Sadiq, and F. Akram, “Skin Cancer Classification Using Image Processing and Machine Learning,” in Proceedings of 18th International Bhurban Conference on Applied Sciences and Technologies, IBCAST 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 439–444. doi: 10.1109/IBCAST51254.2021.9393198.

R. D. Seeja and A. Suresh, “Deep learning based skin lesion segmentation and classification of melanoma using support vector machine (SVM),” Asian Pacific Journal of Cancer Prevention, vol. 20, no. 5, pp. 1555–1561, 2019, doi: 10.31557/APJCP.2019.20.5.1555.

A. L. Kotian, M. Kj, and R. Pt, “Machine Learning-Based Melanoma Skin Cancer Detection,” International Journal of Engineering, Management and Humanities (IJEMH), vol. 4, pp. 72–76, 2023, [Online]. Available: www.ijemh.com

S. S. Samsudin, H. Arof, S. W. Harun, A. W. A. Wahab, and M. Y. I. Idris, “Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern,” PLoS One, vol. 17, no. 9 September, Sep. 2022, doi: 10.1371/journal.pone.0274896.

M. M. Rahman et al., “Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection,” 2022, doi: 10.20944/preprints2.

G. Neela, K. Babu, and V. J. Peter, “SKIN CANCER DETECTION USING SUPPORT VECTOR MACHINE WITH HISTOGRAM OF ORIENTED GRADIENTS FEATURES,” ICTACT JOURNAL ON SOFT COMPUTING, p. 2, 2021, doi: 10.21917/ijsc.2021.0329.

F. Olayah, E. M. Senan, I. A. Ahmed, and B. Awaji, “AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features,” Diagnostics, vol. 13, no. 7, Apr. 2023, doi: 10.3390/diagnostics13071314.

R. Kadirappa, S. Deivalakshmi, R. Pandeeswari, and S. B. Ko, “An automated multi-class skin lesion diagnosis by embedding local and global features of Dermoscopy images,” Multimed Tools Appl, 2023, doi: 10.1007/s11042-023-14892-2.

N. Liu, M. R. Rejeesh, V. Sundararaj, and B. Gunasundari, “ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer,” Expert Syst Appl, vol. 232, p. 120719, Dec. 2023, doi: 10.1016/j.eswa.2023.120719.

Z. Xu, F. R. Sheykhahmad, N. Ghadimi, and N. Razmjooy, “Computer-aided diagnosis of skin cancer based on soft computing techniques,” Open Medicine (Poland), vol. 15, no. 1, pp. 860–871, Jan. 2020, doi: 10.1515/med-2020-0131.

B. Arivuselvam, S. Tanisha, S. Shalini, and V. S. Subhalaksmi, “Skin Cancer Detection And Classification Using Svm Classifier,” 2021.

S. Barburiceanu, R. Terebes, and S. Meza, “3d texture feature extraction and classification using glcm and lbp-based descriptors,” Applied Sciences (Switzerland), vol. 11, no. 5, pp. 1–26, Mar. 2021, doi: 10.3390/app11052332.

Priyanka and D. Kumar, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 1722–1731. doi: 10.1016/j.procs.2020.03.382.

A. H. Farhan and M. Y. Kamil, “Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1757-899X/928/7/072098.

R. C. T. Thakur and V. Panwar, “Skin Cancer Recognition Using SVM Image Processing Technique,” BOHR International Journal of Biocomputing and Nano Technology, vol. 1, no. 1, pp. 21–25, 2022, doi: 10.54646/bijbnt.004.

S. Bakheet, S. Alsubai, A. El-Nagar, and A. Alqahtani, “A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics,” Diagnostics, vol. 13, no. 8, Apr. 2023, doi: 10.3390/diagnostics13081474.

S. Bakheet and A. Al-Hamadi, “A framework for instantaneous driver drowsiness detection based on improved HOG features and naïve bayesian classification,” Brain Sci, vol. 11, no. 2, pp. 1–15, Feb. 2021, doi: 10.3390/brainsci11020240.

W. Xing, N. Deng, B. Xin, Y. Liu, Y. Chen, and Z. Zhang, “Identification of Extremely Similar Animal Fibers Based on Matched Filter and HOG-SVM,” IEEE Access, vol. 7, pp. 98603–98617, 2019, doi: 10.1109/ACCESS.2019.2923225.

O. Attallah and M. Sharkas, “Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories,” Contrast Media Mol Imaging, vol. 2021, 2021, doi: 10.1155/2021/7192016.

R. Mahum and S. Aladhadh, “Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM,” Diagnostics, vol. 12, no. 12, Dec. 2022, doi: 10.3390/diagnostics12122974.

Z. Rustam, Y. Amalia, S. Hartini, and G. S. Saragih, “Linear discriminant analysis and support vector machines for classifying breast cancer,” IAES International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 253–256, 2021, doi: 10.11591/ijai.v10.i1.pp253-256.

M. Q. Khan et al., “Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer,” IEEE Access, vol. 7, pp. 90132–90144, 2019, doi: 10.1109/ACCESS.2019.2926837.

Q. Guo, X. Yu, and G. Ruan, “LPI radar waveform recognition based on deep convolutional neural network transfer learning,” Symmetry (Basel), vol. 11, no. 4, Apr. 2019, doi: 10.3390/sym11040540.

M. Schonlau and R. Y. Zou, “The random forest algorithm for statistical learning,” Stata Journal, vol. 20, no. 1, pp. 3–29, Mar. 2020, doi: 10.1177/1536867X20909688.

V. H. Nhu et al., “Shallow landslide susceptibility mapping by Random Forest base classifier and its ensembles in a Semi-Arid region of Iran,” Forests, vol. 11, no. 4, Apr. 2020, doi: 10.3390/F11040421.

M. Z. Joharestani, C. Cao, X. Ni, B. Bashir, and S. Talebiesfandarani, “PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data,” Atmosphere (Basel), vol. 10, no. 7, Jul. 2019, doi: 10.3390/atmos10070373.

A. Almutairi and R. U. Khan, “Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances,” Applied Sciences, vol. 13, no. 13, p. 7712, Jun. 2023, doi: 10.3390/app13137712.

N. G. Ramadhan and F. D. Adhinata, “Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 3, pp. 1765–1772, Jun. 2022, doi: 10.11591/ijeecs.v26.i3.pp1765-1772.

S. Bechelli and J. Delhommelle, “Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images,” Bioengineering, vol. 9, no. 3, Mar. 2022, doi: 10.3390/bioengineering9030097.

J. Y. Lim, W. N. Tan, and Y. F. Tan, “Anomalous energy consumption detection using a Naïve Bayes approach,” F1000Res, vol. 11, 2022, doi: 10.12688/f1000research.70658.1.