[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122231-en":3,"doc-seo-122231-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122231,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION - Research article","Skin cancer is a leading global cause of death, with incidence rising worldwide, making precision detection and classification essential for better clinical outcomes. The study combines feature extraction methods—Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns—with machine learning algorithms, including Support Vector Machine, Random Forest, and Gaussian Naïve Bayes. Using 17,397 images from the ISIC dataset, results indicate the highest accuracy of 92% when Histogram Oriented Gradients is paired with SVM, and also 92% for multiple GLCM/LBP/HoG with Random Forest combinations.","COMBINATIONS OF FEATURE EXTRACTIONSAND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION  \nA. Muh. Fitrah Asfar1, Mardiyyah Hasnawi*2, Herdianti Darwis3  \n1,2,3Informatics engineering, Computer science, Universitas Muslim Indonesia, Indonesia [Email:](Email:1 muhfitrah.labfik@umi.ac.ud)[1](Email:1 muhfitrah.labfik@umi.ac.ud)[ ](Email:1 muhfitrah.labfik@umi.ac.ud)[muhfitrah.labfik@umi.ac.ud](Email:1 muhfitrah.labfik@umi.ac.ud), [2](2mardiyyah.hasnawi@umi.ac.id)[mardiyyah.hasnawi@umi.ac.id](2mardiyyah.hasnawi@umi.ac.id), [3](3herdianti.darwis@umi.ac.id)[herdianti.darwis@umi.ac.id](3herdianti.darwis@umi.ac.id)  \n(Article received: July 15, 2024; Revision: Auguts 14, 2024; published: December 29, 2024)  \nAbstract  \nOne 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 Cooccurrence 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.  \nKeywords: Feature Extraction, Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, Local Binary Patterns, Skin Cancer.  \n1. INTRODUCTION  \nCancer ranks as the leading cause of death and an obstacle to increasing life expectancy in all parts of the world [1] . According to the results of global cancer statistics (Global Cancer Incidence, Mortality and Prevalence [GLOBOCAN]), a prediction tool that estimates cancer incidence and mortality rates worldwide from the International Agency for Research on Cancer, there is an increasing trend in cancer incidence and mortality rates [2] . In this case, skin cancer is one of the most dangerous diseases found in the world after lung and breast cancer [3] . According to the World Health Organization (WHO), skin cancer is increasing yearly due to exposure to ultraviolet radiation from the sun that passes through the atmosphere and hits human skin [4], [5] . These skin cancers can affect both men and women in fairskinned or light-skinned populations who bear the heaviest burden and are most commonly encountered, namely melanoma skin cancer and non-melanoma skin cancer [6] . There are three main causes of skin cancer: lifestyle, environmental, and genetic [7] . Skin cancer is one type of cancer that can be cured if treated early, but it can be fatal if not treated early and  \nallowed to progress [8] . Therefore, doctors use the usual methodology to determine the type of skin cancer","cbCaittl1lRlb8DT","https://ap.wps.com/l/cbCaittl1lRlb8DT","pdf",931309,1,8,"English","en",105,"# Abstract\n# Introduction\n## Skin cancer burden and need for accurate classification\n## Existing approaches and reported limitations\n# Methodology\n## Feature extraction combinations\n## Machine learning classifiers\n# Experiments\n## Dataset and experimental setup\n# Results and discussion\n## Accuracy of feature-classifier combinations\n# Conclusion","[{\"question\":\"What combinations of feature extraction and machine learning are evaluated for melanoma vs. nevus classification?\",\"answer\":\"The study tests feature extraction methods (GLCM, HOG, and LBP) combined with classifiers such as SVM and Random Forest (and also considers Gaussian Naïve Bayes).\"},{\"question\":\"What dataset size and source are used to train and test the models?\",\"answer\":\"It uses 17,397 images derived from the ISIC dataset.\"},{\"question\":\"Which feature extraction plus classifier achieved the best accuracy, and what was the value?\",\"answer\":\"Histogram Oriented Gradients with Support Vector Machine achieved the highest accuracy of 92%. Several GLCM/LBP/HoG combinations with Random Forest also reached 92%.\"}]","COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION - Research article | PDF",1785809540,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"combinations-of-feature-extractions-and-machine-learning-algorithms-for-skin-cancer-classification-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/combinations-of-feature-extractions-and-machine-learning-algorithms-for-skin-cancer-classification-research-article/122231/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What combinations of feature extraction and machine learning are evaluated for melanoma vs. nevus classification?","Question",{"text":75,"@type":76},"The study tests feature extraction methods (GLCM, HOG, and LBP) combined with classifiers such as SVM and Random Forest (and also considers Gaussian Naïve Bayes).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset size and source are used to train and test the models?",{"text":80,"@type":76},"It uses 17,397 images derived from the ISIC dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Which feature extraction plus classifier achieved the best accuracy, and what was the value?",{"text":84,"@type":76},"Histogram Oriented Gradients with Support Vector Machine achieved the highest accuracy of 92%. 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