{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:53:15Z","timestamp":1776873195261,"version":"3.51.2"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Science Support Project","award":["2022-259"],"award-info":[{"award-number":["2022-259"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.<\/jats:p>","DOI":"10.3390\/diagnostics13091582","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T09:54:53Z","timestamp":1682675693000},"page":"1582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer"],"prefix":"10.3390","volume":"13","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550001, China"},{"name":"Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China"},{"name":"Guizhou Provincial People\u2019s Hospital, Guiyang 550001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yisong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550001, China"},{"name":"Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1097\/CM9.0000000000001474","article-title":"Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020","volume":"134","author":"Cao","year":"2021","journal-title":"Chin. 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