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This study proposes an approach that employs smartphone images, which are preprocessed using adaptive learning and Black-Hat transformation. ViT is utilized for feature extraction, and a stacking model is constructed employing these features in conjunction with image-related variables, like patient age and sex, for final classification. The model\u2019s efficacy in identifying cancer-associated skin diseases was evaluated across six categories of skin lesions: actinic keratosis, basal cell carcinoma, melanoma, nevus, squamous cell carcinoma, and seborrheic keratosis. The suggested model attained an overall accuracy of 97.61%, with a PVV of 96.88%, a recall of 97.63%, and an F1 score of 97.19%, so illustrating its efficacy in detecting malignant skin lesions. This method could greatly aid dermatologists by enhancing diagnostic sensitivity and specificity, reducing delays in identifying the most suspicious lesions, and ultimately reaching more patients in need of timely screenings and patient care, thus saving lives.<\/jats:p>","DOI":"10.1371\/journal.pone.0328402","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T17:28:01Z","timestamp":1753723681000},"page":"e0328402","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost"],"prefix":"10.1371","volume":"20","author":[{"given":"Adil","family":"El Mertahi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hind","family":"Ezzine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8240-660X","authenticated-orcid":true,"given":"Samira","family":"Douzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khadija","family":"Douzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"issue":"2","key":"pone.0328402.ref001","first-page":"2477","article-title":"Skin lesion segmentation and classification using conventional and deep learning based framework","volume":"71","author":"A Bibi","year":"2022","journal-title":"Comput Mater Contin"},{"key":"pone.0328402.ref002","article-title":"Preoperative mapping of nonmelanoma skin cancer using spatial frequency domain and ultrasound imaging","author":"DJ Rohrbach","year":"2014","journal-title":"Acad Radiol"},{"issue":"2","key":"pone.0328402.ref003","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3390\/diagnostics10020102","article-title":"Serous Tubal Intraepithelial Carcinoma: A Concise Review for the Practicing Pathologist and Clinician","volume":"10","author":"SE Bachert","year":"2020","journal-title":"Diagnostics (Basel)"},{"key":"pone.0328402.ref004","doi-asserted-by":"crossref","DOI":"10.1111\/jdv.16533","article-title":"Acral cutaneous lesions in the time of covid-19","volume":"34","author":"S Recalcati","year":"2020","journal-title":"J Eur Acad Dermatol Venereol"},{"issue":"6","key":"pone.0328402.ref005","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1109\/TIM.2012.2192349","article-title":"Using adaptive thresholding and skewness correction to detect gray areas in melanoma in situ images","volume":"61","author":"G Sforza","year":"2012","journal-title":"IEEE Trans Instrum Meas"},{"issue":"2012","key":"pone.0328402.ref006","first-page":"366","article-title":"Cancer facts and Figs 2014","volume":"16","author":"T Tarver","year":"2012","journal-title":"J Consum Health Internet"},{"issue":"12","key":"pone.0328402.ref007","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/JBHI.2021.3067789","article-title":"Multi-Class Skin Lesion Detection and Classification via Teledermatology","volume":"25","author":"MA Khan","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"issue":"6","key":"pone.0328402.ref008","first-page":"412","article-title":"Differentiating malignant melanoma from other lesions using dermoscopy","volume":"65","author":"R Gniadecki","year":"2019","journal-title":"Can Fam Physician"},{"key":"pone.0328402.ref009"},{"issue":"4","key":"pone.0328402.ref010","doi-asserted-by":"crossref","first-page":"700","DOI":"10.3390\/cancers16040700","article-title":"Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment","volume":"16","author":"MF Ijaz","year":"2024","journal-title":"Cancers (Basel)"},{"key":"pone.0328402.ref011","doi-asserted-by":"crossref","first-page":"100036","DOI":"10.1016\/j.mlwa.2021.100036","article-title":"An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models","volume":"5","author":"MS Ali","year":"2021","journal-title":"Machine Learning with Applications"},{"key":"pone.0328402.ref012","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 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