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This study develops a hierarchical approach that organizes multiple binary CNN models to classify skin conditions across Benign and Malignant groups, reducing task complexity. MobileNetV2, EfficientNet-B0, ResNet-18, and ResNet-50 are compared using recall as the primary metric to limit false negatives. Results show 82.62% recall for the hierarchical model versus 60.27% for a single six-class model, alongside improved accuracy, precision, F1-score, and balanced accuracy, supporting better clinical decision-support generalizability.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/hierarchical-deep-learning-framework-for-skin-disease-and-cancer-classification-performance-enhancement/356771/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/hierarchical-deep-learning-framework-for-skin-disease-and-cancer-classification-performance-enhancement/356771.png","ImageObject",300,407,{"name":92,"@type":93},"Putri","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the hierarchical framework address in skin disease classification?","Question",{"text":112,"@type":113},"It targets the difficulty of accurately distinguishing similar skin lesions and improving classification performance for multiple skin conditions spanning Benign and Malignant groups.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the hierarchical method differ from direct multi-class classification?",{"text":117,"@type":113},"Instead of using a single model for multi-class classification, it organizes multiple binary classification models to reduce task complexity and improve overall performance.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is recall emphasized in this study’s evaluation?",{"text":121,"@type":113},"Recall is treated as the primary metric to minimize false negatives, which is especially important for early detection in skin cancer contexts.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},356771,1790187454,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31},962085571259,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Article  \nHierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement  \nChanapa Chaitan 1, Sasithorn Tengjongdee 1, Suejit Pechprasarn 1,2 and Kitsada Thadson 2, *  \nAcademic Editor: Ali Farzamnia  \nReceived: 28 March 2026  \nRevised: 22 April 2026  \nAccepted: 27 April 2026  \nPublished: 1 May 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 College of Biomedical Engineering, Rangsit University, Pathum Thani 12000, Thailand; [chanapa.c64@rsu.ac.th](chanapa.c64@rsu.ac.th) (C.C.); [sasithorn.t65@rsu.ac.th](sasithorn.t65@rsu.ac.th) (S.T.); [suejit.p@rsu.ac.th](suejit.p@rsu.ac.th) (S.P.)  \n2 Center of Excellence in Artificial Intelligence & Supercomputing, Rangsit University, Pathum Thani 12000, Thailand  \n* Correspondence: [kitsada.t@rsu.ac.th](kitsada.t@rsu.ac.th); Tel.: +66-8-0511-0788  \nAbstract  \nCurrently, the number of people who have been investigated for skin cancer has increased significantly worldwide. For prior diagnosis, dermatologists can typically visually inspect skin lesions for abnormalities. However, an expert is required, and the similarity of some skin lesions remains challenging. This study aimed to address the challenge of classifying multiple images of skin conditions, including both Benign and Malignant groups, using the hierarchical method. Instead of directly performing multi-class classification using a single model, multiple binary classification models were organized to reduce task complexity and improve overall performance. In the methodology, four convolutional neural network (CNN) models, namely MobileNetV2, EfficientNet-B0, ResNet-18, and ResNet-50, were selected as candidates for this problem. The proposed hierarchical binary classification model was evaluated against conventional multi-class classification methods. As a result, various evaluation metrics were used to assess model performance, with recall as the primary metric in this study, given the emphasis on minimizing false negatives. However, some results revealed discrepancies between the highest recall and other performance metrics. Further analysis demonstrated the potential of using recall as a selection criterion for identifying the most suitable CNN models. The single model-based classification of six classes of skin lesion images achieves the highest recall of 60.27% with MobileNetV2 . Meanwhile, the proposed hierarchical model achieves a higher recall of 82.62%, representing a significant increase of 22.35% . Additionally, improvements were observed across all other evaluation metrics, including accuracy (+25.46%), precision (+17.21%), F1-score (+21.34%), balanced accuracy (+12.69%), specificity (+3.03%), and G-mean (+14.25%) . These improvements indicate enhanced performance in correctly identifying both positive and negative cases, while reducing misclassification rates. This outcome demonstrates the potential to improve the model’s generalizability, thereby increasing its applicability across various clinical decision-support systems.  \nKeywords: multi-class classification; hierarchical binary classification; skin cancer; deep learning; convolutional neural networks  \n1. Introduction  \nEpidemiological studies have shown a consistent global rise in skin cancer cases over recent decades [1,2] . By 2024, the number of new cases worldwide is expected to exceed 200,000 [3] . Although Thailand has a lower incidence rate compared to other  \ntropical countries, the trend continues to rise year after year [4] . Dermatological conditions represent a clinically diverse spectrum of diseases [5], ranging from benign disorders such as seborrheic keratosis to lesions with malignant potential, including nevus and actinic keratosis. Changes in skin conditions, such as chronic wounds, and noticeable alterations in the size, color, or shape of","cbCaiiYcOUOUbAAW","https://ap.wps.com/l/cbCaiiYcOUOUbAAW","pdf",2667639,"English","# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the hierarchical framework address in skin disease classification?\",\"answer\":\"It targets the difficulty of accurately distinguishing similar skin lesions and improving classification performance for multiple skin conditions spanning Benign and Malignant groups.\"},{\"question\":\"How does the hierarchical method differ from direct multi-class classification?\",\"answer\":\"Instead of using a single model for multi-class classification, it organizes multiple binary classification models to reduce task complexity and improve overall performance.\"},{\"question\":\"Why is recall emphasized in this study’s evaluation?\",\"answer\":\"Recall is treated as the primary metric to minimize false negatives, which is especially important for early detection in skin cancer contexts.\"}]","Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement | PDF",1790127215]