[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118543-en":3,"doc-seo-118543-105":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},118543,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification","Skin diseases create significant diagnostic challenges that require high precision to support evaluation and influence treatment selection. Medical imaging is central to the diagnostic workflow, and machine learning can classify skin diseases from image data with strong reported accuracy. This study uses a Systematic Literature Review (SLR) to survey machine learning algorithms for image-based skin disease classification, applying a Boolean search strategy in Scopus with screening via predefined inclusion and exclusion criteria. Results indicate Convolutional Neural Networks (CNNs) are most frequently used and achieve the highest classification accuracy.","A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification  \nFadilah Karamun Nisaa Nadiyah 1, Nayla Nur Alifah2, Sri Nurdiati3, Elis Khatizah4,  \nMohamad Khoirun Najib5,*  \n1,2,3,4,5Applied Mathematics, School of Data Science, Mathematics and Informatics, IPB  \nUniversity, Bogor 16680, Indonesia  \n[E-mail : fadilahkaramun.nisaa@apps.ipb.ac.id](E-mail : fadilahkaramun.nisaa@apps.ipb.ac.id1)[1](E-mail : fadilahkaramun.nisaa@apps.ipb.ac.id1), [naylalifah@apps.ipb.ac.id](naylalifah@apps.ipb.ac.id2)[2](naylalifah@apps.ipb.ac.id2), [nurdiati@apps.ipb.ac.id](nurdiati@apps.ipb.ac.id3)[3](nurdiati@apps.ipb.ac.id3), [elis_khatizah@apps.ipb.ac.id](elis_khatizah@apps.ipb.ac.id4)[4](elis_khatizah@apps.ipb.ac.id4), [mkhoirun@apps.ipb.ac.id](mkhoirun@apps.ipb.ac.id5)[5](mkhoirun@apps.ipb.ac.id5),*  \n*Corresponding author  \nReceived 2 May 2025; Revised 10 May 2025; Accepted 13 May 2025  \nAbstract-Skin diseases pose health challenges that necessitate precise diagnosis for evaluation, ultimately influencing treatment choices. Medical imaging plays a vital role in the diagnostic procedure. Machine learning technology can aid in the classification of skin diseases through image data, achieving notable accuracy in diagnosis. This research aims to explore the machine learning algorithms that can be employed to create systems for classifying skin diseases based on images. The methodology used is a Systematic Literature Review (SLR), which serves to provide an extensive overview of how machine learning is applied in the classification of skin diseases. The literature search approach utilized the Boolean method, specifically applied to the Scopus database. The chosen articles underwent screening based on established inclusion and exclusion criteria. The findings reveal that the Convolutional Neural Network (CNN) is the most commonly used machine learning algorithm, which has demonstrated the highest classification accuracy.  \nKeywords-Skin Disease, Machine Learning, Classification, CNN.  \n1. INTRODUCTION  \nSkin diseases are represent significant global health challenge. That is complicated by their intricate nature and the extensive time required for accurate diagnosis. Imaging plays a crucial role, serving as a foundational element prior to any surgical or treatment decisions. It enables the establishment of preliminary knowledge and facilitates accurate diagnoses [1] . Consequently, medical imaging has evolved into an essential tool that initiates the treatment process for various diseases, encompassing stages from detection to evaluation and ultimately leading to treatment decisions. In particular, skin diseases represent a medical domain where imaging significantly contributes to the detection, diagnosis, and management of conditions [2] .  \nComputer-aided automatic identification of skin diseases from images can minimize human error and speed up the detection process, thereby assisting clinicians in making diagnoses more efficiently and facilitating timely patient treatment. Typically, there are two main methodological frameworks used. The first is the traditional method, which relies on manually defined features such as color and texture for the identification and detection of skin diseases. However, the selection of relevant features is time-consuming and crucial, as it directly affects classification accuracy. The second framework is an evolutionary approach that incorporates artificial intelligence (AI) and deep learning techniques. This approach enables automatic and effective feature learning of skin disease characteristics by utilizing established image segmentation algorithms, which categorize images based on pixel intensity, edges, and regions.[3-6] .  \nOn the other side, machine learning and image processing techniques can help achieve high accuracy in skin diagnosing at the initial stage. Images processing plays an effective role in diagnosis the skin diseases. Machine learning algorithms can used for","cbCaic5rJWVluRkR","https://ap.wps.com/l/cbCaic5rJWVluRkR","pdf",488107,1,14,"English","en",105,"# Introduction\n## Skin disease challenges and the role of imaging\n## Methodological frameworks: traditional features vs AI/deep learning\n## Evolution of classification approaches: traditional ML and transformers\n## CNN architectures and image-processing techniques","[{\"question\":\"What is the main objective of the systematic literature review?\",\"answer\":\"To explore machine learning algorithms used to build image-based systems for classifying skin diseases, using an SLR approach to summarize how machine learning is applied in this domain.\"},{\"question\":\"Which dataset and search strategy are used in the literature search?\",\"answer\":\"The literature search uses a Boolean method applied to the Scopus database, followed by screening based on inclusion and exclusion criteria.\"},{\"question\":\"Which machine learning algorithm is reported as most commonly used and highest accuracy?\",\"answer\":\"Convolutional Neural Networks (CNNs) are the most commonly used algorithm and are reported to demonstrate the highest classification accuracy.\"}]","A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification | 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