[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120374-en":3,"doc-seo-120374-105":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},120374,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Three Machine Learning Techniques for Melanoma Cancer Detection - Research Paper","Machine learning techniques are increasingly used for cancer detection because they can reach high diagnostic accuracy. Melanoma, a common and dangerous skin cancer, can be better prevented when detected at early stages, where clinicians face challenges and diagnostic difficulty. This work provides an open-source tutorial for diagnosing melanoma from dermatoscopic images by preprocessing, enhancing contrast and lesion boundaries, extracting skin features, and comparing ANN, SVM, and KNN. Results show ANN achieves 83.5% accuracy and the code is shared for improvement and learning.","Three Machine Learning Techniques for Melanoma Cancer Detection  \nHadi Naghavipour *  \n*Corresponding Author, UNITAR Graduate School, UNITAR International University, Malaysia.  \nE-mail: [email@hadi-naghavipour.com](email@hadi-naghavipour.com)  \nGholamReza Zandi  \nUniversiti Kuala Lumpur (UniKL) Business School, [Malaysia. E-mail:zandi@unikl.edu.my](Malaysia. E-mail:zandi@unikl.edu.my)  \nAbdulaziz Al-Nahari  \nUNITAR Graduate School, UNITAR International University, Malaysia. E-mail: [abdulaziz.yahya@unitar.my](abdulaziz.yahya@unitar.my).  \nAbstract  \nThe application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fatal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an opensource tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a DullRazor preprocessing method to remove extra details such as hair. Next, histogram adjustmentsand lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) . The results show that ANN could achieve better accuracy (83.5%) . In order to mitigate the biases in existing studies, the source code of this research is available at [hadi-naghavipour.com/ml](hadi-naghavipour.com/ml) to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.  \nKeywords: Artificial Neural Network, Multi-Layer Perceptron, Support Vector Machine, KNearest, Skin Cancer, Image Processing.  \n\n| Journal of Information Technology Management, 2023, Vol. 15, Issue 2, pp. 59-72. | Received: January 22, 2023 |\n| --- | --- |\n| Published by University of Tehran, Faculty of Management | Received in revised form: February 02, 2023 |\n| doi: [https://doi.org/ 10.22059/jitm.2023.92337](https://doi.org/ 10.22059/jitm.2023.92337) | Accepted: March 25, 2023 |\n| Article Type: Research Paper\u003Cbr>© Authors | Published online: May 23, 2023\u003Cbr> |\n\nIntroduction  \nSkin cancer is one of the most common cancer and is projected to increase in incidence over the coming decades. Melanoma is the most dangerous type of skin cancer, and with the growth of the lesion, the chances of treatment are significantly reduced. Diagnosis of melanoma in the early stages can reduce or prevent mortality; however, diagnosis is fraught with severe technicality. It is fallen on technology management specialists to get to know the latest technology development in a hands-on capacity to unleash the potential of artificial intelligence in health sectors. Adopting and managing technologies requires a comprehensive understanding of how these technologies can contribute to solving problems that otherwise were not feasible to overcome. Skin cancer detection is one prime example due to the severe challenge exposed to human experts for early-stage diagnosis. Since diagnosing the disease in the first stages is complex, even by expert specialists, providing an effective method for diagnosing melanoma's early stages is highly beneficial and valuable (Leiter, Keim, & Garbe, 2020) . The analysis of damaged skin tissue is considered one of the non-native techniques for diagnosing skin complications. The traditional cancer diagnosis methods involve a dermatoscopy of skin tissue images, which requires experie","cbCaidYxW5xDQt8W","https://ap.wps.com/l/cbCaidYxW5xDQt8W","pdf",884777,1,14,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n## Preprocessing and Lesion Boundary Enhancement\n## Feature Extraction and Classification\n# Results and Discussion\n# Future Works\n# Conclusion","[{\"question\":\"How does the proposed approach prepare dermatoscopic images for melanoma detection?\",\"answer\":\"It uses a DullRazor preprocessing step to remove extra details such as hair, then applies histogram adjustments and lighting thresholds to increase contrast and select lesion boundaries.\"},{\"question\":\"Which machine learning methods are compared for melanoma classification?\",\"answer\":\"The study compares Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) using extracted features from skin tissue.\"},{\"question\":\"What classification accuracy does the best-performing method achieve?\",\"answer\":\"The results indicate that ANN achieves better accuracy at 83.5%.\"}]","Three Machine Learning Techniques for Melanoma Cancer Detection - Research Paper | PDF",1785729724,35,{"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},"three-machine-learning-techniques-for-melanoma-cancer-detection-research-paper","",{"@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/three-machine-learning-techniques-for-melanoma-cancer-detection-research-paper/120374/",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","2026-08-03",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},"How does the proposed approach prepare dermatoscopic images for melanoma detection?","Question",{"text":75,"@type":76},"It uses a DullRazor preprocessing step to remove extra details such as hair, then applies histogram adjustments and lighting thresholds to increase contrast and select lesion boundaries.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are compared for melanoma classification?",{"text":80,"@type":76},"The study compares Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) using extracted features from skin tissue.",{"name":82,"@type":73,"acceptedAnswer":83},"What classification accuracy does the best-performing method achieve?",{"text":84,"@type":76},"The results indicate that ANN achieves better accuracy at 83.5%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]