{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:15:58Z","timestamp":1792080958915,"version":"4.4.0"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Mongkut\u2019s Institute of Technology Ladkrabang (KMITL) through the KMITL Research and Innovation Services","award":["164226"],"award-info":[{"award-number":["164226"]}]},{"DOI":"10.13039\/501100004704","name":"National Research Council of Thailand","doi-asserted-by":"crossref","award":["38700"],"award-info":[{"award-number":["38700"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Public Health, Thailand, the Program Management Unit for Competi-tiveness","award":["175749"],"award-info":[{"award-number":["175749"]}]},{"name":"Technology and Innovation-Based Enterprise Development Fund","award":["183091"],"award-info":[{"award-number":["183091"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 \u00b1 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications.<\/jats:p>","DOI":"10.3390\/cancers18010070","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T02:07:58Z","timestamp":1766714878000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mammogram Analysis with YOLO Models on an Affordable Embedded System"],"prefix":"10.3390","volume":"18","author":[{"given":"Anongnat","family":"Intasam","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Piyawattanametha","sequence":"additional","affiliation":[{"name":"School of Engineering, Michigan State University, East Lansing, MI 48823, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuttachon","family":"Promworn","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Titipon","family":"Jiranantanakorn","sequence":"additional","affiliation":[{"name":"Diagnostic Radiology and Nuclear Medicine Department, The National Cancer Institute, Ratchatewi, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soonthorn","family":"Thawornwanchai","sequence":"additional","affiliation":[{"name":"Diagnostic Radiology and Nuclear Medicine Department, The National Cancer Institute, Ratchatewi, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pakpawee","family":"Pichayakul","sequence":"additional","affiliation":[{"name":"Diagnostic Radiology and Nuclear Medicine Department, The National Cancer Institute, Ratchatewi, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarawan","family":"Sriwanichwiphat","sequence":"additional","affiliation":[{"name":"Diagnostic Radiology and Nuclear Medicine Department, The National Cancer Institute, Ratchatewi, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somchai","family":"Thanasitthichai","sequence":"additional","affiliation":[{"name":"Diagnostic Radiology and Nuclear Medicine Department, The National Cancer Institute, Ratchatewi, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirihattaya","family":"Khwayotha","sequence":"additional","affiliation":[{"name":"Udon Thani Cancer Hospital, Muang, Udon Thani 41300, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Methininat","family":"Lertkowit","sequence":"additional","affiliation":[{"name":"Udon Thani Cancer Hospital, Muang, Udon Thani 41300, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nucharee","family":"Phakwapee","sequence":"additional","affiliation":[{"name":"Udon Thani Cancer Hospital, Muang, Udon Thani 41300, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aniwat","family":"Juhong","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823, USA"},{"name":"Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2228-8485","authenticated-orcid":false,"given":"Wibool","family":"Piyawattanametha","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand"},{"name":"Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","first-page":"477","article-title":"Breast cancer statistics 2024","volume":"74","author":"Giaquinto","year":"2024","journal-title":"CA A Cancer J. 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