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This study presents a novel algorithm, Sigmoidal Slope Analysis and Aspect Ratio Evaluation (SAAR), designed for real-time application on edge devices. By employing a multi-step adaptive process with sigmoidal functions, SAAR enhances intensity contrast and prioritizes regions of interest, enabling fast, accurate detection of microcalcifications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to develop and validate an efficient, edge-device-compatible method for detecting microcalcifications in mammographic images. The goal is to provide a tool that enhances diagnostic efficiency through real-time processing, thereby supporting early breast cancer detection in both clinical and remote settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The SAAR algorithm utilizes an adaptive slope detection technique based on the sigmoid function, dynamically adjusting to local intensity features. This approach allows for greater adaptability to image variations. The algorithm prioritizes regions of interest through a multi-step adaptive process, enhancing intensity differences to focus on potential microcalcifications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Testing on established mammography databases, such as MIAS, demonstrates the algorithm's effectiveness, with improved sensitivity compared to conventional methods. Designed for edge devices, the algorithm leverages their real-time processing capabilities, offering lower latency and enhanced privacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The integration of SAAR with edge devices represents a promising advancement in breast cancer detection. The adaptive nature of SAAR, coupled with the real-time processing capabilities of edge devices, provides a robust solution for enhancing microcalcification detection efficiency and sensitivity in mammography.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261442665","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:32:03Z","timestamp":1777908723000},"page":"563-577","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-Time microcalcification detection with SAAR algorithm"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3043-6879","authenticated-orcid":false,"given":"Aleksandar","family":"Peulic","sequence":"first","affiliation":[{"name":"Department of Mathematics and Informatics,\u00a0University of Kragujevac, Faculty of Science, Radoja Domanovica 12, Kragujevac, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5401-8634","authenticated-orcid":false,"given":"Zeljko","family":"Jovanovic","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering,\u00a0University of Kragujevac, Faculty of Technical Sciences Cacak, Svetog Save 65, Cacak, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7927-1169","authenticated-orcid":false,"given":"Marina","family":"Milosevic","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering,\u00a0University of Kragujevac, Faculty of Technical Sciences Cacak, Svetog Save 65, Cacak, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101912"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101953"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.11.010"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-140841"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-220235"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-171336"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(03)00004-9"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21144854"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2018.11.167"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13010161"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"e_1_3_3_13_2","first-page":"24","article-title":"Application of the wavelet transform to automated detection of clustered microcalcifications in digital mammograms","volume":"16","author":"Yoshida H","year":"1994","unstructured":"Yoshida H, Doi K, Nishikawa R, et al. 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