{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:11:54Z","timestamp":1791486714146,"version":"4.3.4"},"reference-count":87,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,5,11]]},"abstract":"<jats:p>Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process\u2019s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.<\/jats:p>","DOI":"10.1155\/2022\/5667264","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:06:07Z","timestamp":1652313967000},"page":"1-17","source":"Crossref","is-referenced-by-count":48,"title":["PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods"],"prefix":"10.1155","volume":"2022","author":[{"given":"Ashkan","family":"Nomani","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasaman","family":"Ansari","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Hossein","family":"Nasirpour","sequence":"additional","affiliation":[{"name":"Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Masoumian","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, Universitat Rovira I Virgili, Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsan Sadeghi","family":"Pour","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Kashan Branch, Islamic Azad University, Kashan 8715998151, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7910-718X","authenticated-orcid":true,"given":"Amin","family":"Valizadeh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdz051"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0207558"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1186\/bcr1639"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s10555-015-9551-7"},{"issue":"5","key":"5","first-page":"519M","article-title":"Metastatic breast cancer","volume":"88","author":"O. 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