{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T23:28:34Z","timestamp":1771370914576,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"by The Science, Technology & Innovation Funding Authority"},{"name":"Automated Abnormalities Detection in Mammography"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices.<\/jats:p>","DOI":"10.1007\/s40747-024-01532-x","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T14:03:22Z","timestamp":1720706602000},"page":"7279-7295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automated abnormalities detection in mammography using deep learning"],"prefix":"10.1007","volume":"10","author":[{"given":"Ghada M.","family":"El-Banby","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3844-4780","authenticated-orcid":false,"given":"Nourhan S.","family":"Salem","sequence":"additional","affiliation":[]},{"given":"Eman A.","family":"Tafweek","sequence":"additional","affiliation":[]},{"given":"Essam N. Abd","family":"El-Azziz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"1532_CR1","unstructured":"World health organization, a. URL https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/breast-cancer. Accessed 26 Mar 2022"},{"key":"1532_CR2","unstructured":"American cancer society, b. URL https:\/\/www.cancer.org\/cancer\/breast-cancer.html. Accessed 01 May 2022"},{"key":"1532_CR3","doi-asserted-by":"publisher","unstructured":"Hamed G, Marey M, Amin S, Tolba M (2021) Comparative study and analysis of recent computer aided diagnosis systems for masses detection in mammograms. Int J Intell Comput Inf Sci 21:33\u201348. https:\/\/doi.org\/10.21608\/ijicis.2021.56425.1050","DOI":"10.21608\/ijicis.2021.56425.1050"},{"key":"1532_CR4","doi-asserted-by":"publisher","unstructured":"Bozek J, Mustra M, Delac K , Grgic M (2009) A Survey of image processing algorithms in digital mammography, pages 631\u2013657. https:\/\/doi.org\/10.1007\/978-3-642-02900-4_24","DOI":"10.1007\/978-3-642-02900-4_24"},{"key":"1532_CR5","doi-asserted-by":"publisher","first-page":"33438","DOI":"10.1109\/ACCESS.2021.3058773","volume":"9","author":"BA Reddy","year":"2021","unstructured":"Reddy BA, Sami A, Mirjam J, Bharanidharan S, Krishnan K, Adnan A (2021) Preprocessing of breast cancer images to create datasets for deep-cnn. IEEE Access 9:33438\u201333463. https:\/\/doi.org\/10.1109\/ACCESS.2021.3058773","journal-title":"IEEE Access"},{"key":"1532_CR6","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.addr.2014.07.009","volume":"76","author":"LS Yong","year":"2014","unstructured":"Yong LS, Ik JS, Seulhee J, Jae CI, Cheol-Hee A (2014) Targeted multimodal imaging modalities. Adv Drug Deliv Rev 76:60\u201378. https:\/\/doi.org\/10.1016\/j.addr.2014.07.009","journal-title":"Adv Drug Deliv Rev"},{"issue":"8","key":"1532_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3449","volume":"37","author":"A El-Hag Noha","year":"2021","unstructured":"El-Hag Noha A, Ahmed S, El-Banby Ghada M, Walid El-Shafai, Khalaf Ashraf AM, Waleed Al-Nuaimy, El-Samie Fathi Abd E, El-Hoseny Heba M (2021) Utilization of image interpolation and fusion in brain tumor segmentation. Int J Numer Methods Biomed Eng 37(8):e3449. https:\/\/doi.org\/10.1002\/cnm.3449","journal-title":"Int J Numer Methods Biomed Eng"},{"key":"1532_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/s21196655","author":"H Michael","year":"2021","unstructured":"Michael H, Subrata C, Biswajeet P, Manoranjan P, Douglas G, Anwaar U-H, Abdullah A (2021) Deep mining generation of lung cancer malignancy models from chest x-ray images. Sensors. https:\/\/doi.org\/10.3390\/s21196655","journal-title":"Sensors"},{"key":"1532_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/s23146585","author":"J Horry Michael","year":"2023","unstructured":"Horry Michael J, Subrata C, Biswajeet P, Manoranjan P, Jing Z, Wen LH, Datta BP, Rajendra AU (2023) Development of debiasing technique for lung nodule chest x-ray datasets to generalize deep learning models. Sensors. https:\/\/doi.org\/10.3390\/s23146585","journal-title":"Sensors"},{"key":"1532_CR10","doi-asserted-by":"publisher","unstructured":"El-Shafai W, El-Hag Noha A, El-Banby Ghada M, Khalaf Ashraf AM, Soliman Naglaa F, Algarni Abeer D, El-Samie Fathi E. Abd (2021) An efficient cnn-based automated diagnosis framework from covid-19 ct images. Comput Mater Continua 69(1), 1323\u20131341. https:\/\/doi.org\/10.32604\/cmc.2021.017385","DOI":"10.32604\/cmc.2021.017385"},{"issue":"3","key":"1532_CR11","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1002\/jemt.23596","volume":"84","author":"A El-Hag Noha","year":"2021","unstructured":"El-Hag Noha A, Ahmed S, Walid E-S, El-Hoseny Heba M, Khalaf Ashraf AM, El-Fishawy Adel S, Waleed A-N, Abd El-Samie Fathi E, El-Banby Ghada M (2021) Classification of retinal images based on convolutional neural network. Microsc Res Techn 84(3):394\u2013414. https:\/\/doi.org\/10.1002\/jemt.23596","journal-title":"Microsc Res Techn"},{"key":"1532_CR12","doi-asserted-by":"crossref","unstructured":"Khalil Hager, El-Hag Noha A, Sedik Ahmed, El-Shafai Walid, Mohamed Abd, Khalaf Ashraf AM, El-Fishawy Adel, El\u00a0Banby Ghada, El-Samie Fathi Abd (2019) Classification of diabetic retinopathy types based on convolution neural network (cnn). 12","DOI":"10.21608\/mjeer.2019.76962"},{"key":"1532_CR13","doi-asserted-by":"publisher","first-page":"4","DOI":"10.4103\/0971-3026.95396","volume":"22","author":"R Varma Dandu","year":"2012","unstructured":"Varma Dandu R (2012) Managing dicom images: tips and tricks for the radiologist. Indian J Radiol Imaging 22:4\u201313. https:\/\/doi.org\/10.4103\/0971-3026.95396","journal-title":"Indian J Radiol Imaging"},{"key":"1532_CR14","doi-asserted-by":"publisher","unstructured":"Novaes Magdala de\u00a0A (2020) Telecare within different specialties, pages 185\u2013254. Elsevier, https:\/\/doi.org\/10.1016\/B978-0-12-814309-4.00010-0","DOI":"10.1016\/B978-0-12-814309-4.00010-0"},{"key":"1532_CR15","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.inffus.2021.07.001","volume":"76","author":"S Wang","year":"2021","unstructured":"Wang S, Emre CM, Zhang Y-D, Yu X, Lu S, Yao X, Zhou Q, Miguel M-G, Tian Y, Gorriz Juan M, Tyukin I (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Inf Fusion 76:376\u2013421. https:\/\/doi.org\/10.1016\/j.inffus.2021.07.001","journal-title":"Inf Fusion"},{"key":"1532_CR16","doi-asserted-by":"publisher","unstructured":"da\u00a0Silva E, Mendonca G (2005) Digital image processing, pages 891\u2013910. 12. ISBN 9780121709600. https:\/\/doi.org\/10.1016\/B978-012170960-0\/50064-5","DOI":"10.1016\/B978-012170960-0\/50064-5"},{"key":"1532_CR17","doi-asserted-by":"publisher","unstructured":"Susama B, Kim Gaik T, Audrey H, Sanjoy Kumar D. Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review. Int J Electr Comput Eng (IJECE), 10:2336, 2020. https:\/\/doi.org\/10.11591\/ijece.v10i3.pp2336-2348","DOI":"10.11591\/ijece.v10i3.pp2336-2348"},{"key":"1532_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107880","volume":"243","author":"F Maryam","year":"2024","unstructured":"Maryam F, Subrata C, Biswajeet P, Oliver F, Datta BP, Hossein C, Rajendra A (2024) Deep learning techniques in pet\/ct imaging: acomprehensive review from sinogram to image space. Comput Methods Progr Biomed 243:107880. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107880","journal-title":"Comput Methods Progr Biomed"},{"key":"1532_CR19","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1155\/2022\/3823350","volume":"3823350","author":"S Shehzadi","year":"2022","unstructured":"Shehzadi S, Hassan Muhammad A, Rizwan M, Kryvinska N, Vincent K (2022) Diagnosis of chronic ischemic heart disease using machine learning techniques. Comput Intell Neurosci 3823350:2022. https:\/\/doi.org\/10.1155\/2022\/3823350","journal-title":"Comput Intell Neurosci"},{"key":"1532_CR20","unstructured":"Deshpande N\u00a0M, Gite S, Pradhan B, Kotecha K, Alamri A (2022) Improved otsu and kapur approach for white blood cells segmentation based on lebtlbo optimization for the detection of leukemia. Math Biosci Eng,"},{"key":"1532_CR21","doi-asserted-by":"crossref","unstructured":"Liu J, Lei J, Ou Y, Zhao Y, Tuo X, Zhang B, Shen M (2022) Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clin Exp Med 23(3):1\u201316. https:\/\/doi.org\/10.1007\/s10238-022-00895-0","DOI":"10.1007\/s10238-022-00895-0"},{"key":"1532_CR22","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.3934\/mbe.2022060","volume":"19","author":"R Roslidar","year":"2022","unstructured":"Roslidar R, Mohd S, Khairun S, Biswajeet P, Fitri A, Maimun S, Khairul M (2022) Breacnet: a high-accuracy breast thermogram classifier based on mobile convolutional neural network. Math Biosci Eng 19:1304\u20131331. https:\/\/doi.org\/10.3934\/mbe.2022060","journal-title":"Math Biosci Eng"},{"key":"1532_CR23","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.compbiomed.2021.104966","volume":"139","author":"M Webb Jeremy","year":"2021","unstructured":"Webb Jeremy M, Adusei Shaheeda A, Yinong W, Naziya S, Kalie A, Meixner Duane D, Fazzio Robert T, Mostafa F, Azra A (2021) Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging. Comput Biol Med 139:12. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104966","journal-title":"Comput Biol Med"},{"key":"1532_CR24","doi-asserted-by":"crossref","unstructured":"Hirsch L, Huang Y, Luo S, Rossi Saccarelli C, Lo Gullo R, Daimiel Naranjo I, Bitencourt AG, Onishi N, Ko ES, Leithner D, Avendano D, Eskreis-Winkler S, Hughes M, Martinez DF, Pinker K, Juluru K, AE El-Rowmeim, Elnajjar P, Morris EA, LC Parra, Sutton EJ (2021) Radiologist-Level Performance Using Deep Learning for Segmentation of Breast Cancers on MRI. Radiol: Artif Intell 4(1). https:\/\/doi.org\/10.1148\/ryai.200231","DOI":"10.1148\/ryai.200231"},{"issue":"5","key":"1532_CR25","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab5745.","volume":"65","author":"H Sun","year":"2020","unstructured":"Sun H, Li C, Liu B, Liu Z, Wang M, Zheng H, Feng DD, Wang S (2020) Aunet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms. Phys Med Biol 65(5):055005. https:\/\/doi.org\/10.1088\/1361-6560\/ab5745.","journal-title":"Phys Med Biol"},{"issue":"10","key":"1532_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abfa35","volume":"66","author":"H Xuan","year":"2021","unstructured":"Xuan H, Yunpeng B, Yefan X, Ying L (2021) Mass segmentation for whole mammograms via attentive multi-task learning framework. Phys Med Biol 66(10):105015. https:\/\/doi.org\/10.1088\/1361-6560\/abfa35","journal-title":"Phys Med Biol"},{"issue":"2","key":"1532_CR27","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.bbe.2021.03.005","volume":"41","author":"Y Yan","year":"2021","unstructured":"Yan Y, Conze P-H, Quellec G, Lamard M, Cochener B, Coatrieux G (2021) Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention. Biocybern Biomed Eng 41(2):746\u2013757","journal-title":"Biocybern Biomed Eng"},{"key":"1532_CR28","doi-asserted-by":"publisher","first-page":"59037","DOI":"10.1109\/ACCESS.2019.2914873","volume":"7","author":"Shuyi Li","year":"2019","unstructured":"Li Shuyi, Dong Min, Guangming Du, Xiaomin Mu (2019) Attention dense-u-net for automatic breast mass segmentation in digital mammogram. IEEE Access 7:59037\u201359047","journal-title":"IEEE Access"},{"key":"1532_CR29","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1007\/s10278-020-00330-4","volume":"33","author":"A Zeiser Felipe","year":"2020","unstructured":"Zeiser Felipe A, da Costa CA, Zonta T, Marques NMC, Roehe AV, Moreno M, da Rosa RR (2020) Segmentation of masses on mammograms using data augmentation and deep learning. J Digit Imaging 33:858\u2013868","journal-title":"J Digit Imaging"},{"key":"1532_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105093","volume":"140","author":"V Joel","year":"2022","unstructured":"Joel V, Vilanova Joan C, Robert M et al (2022) A u-net ensemble for breast lesion segmentation in dce mri. Comput Biol Med 140:105093","journal-title":"Comput Biol Med"},{"key":"1532_CR31","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.procs.2015.06.079","volume":"54","author":"S Anuj Kumar","year":"2015","unstructured":"Anuj Kumar S, Bhupendra G (2015) A novel approach for breast cancer detection and segmentation in a mammogram. Procedia Comput Sci 54:676\u2013682","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"1532_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4103\/2153-3539.109802","volume":"4","author":"AM Khan","year":"2013","unstructured":"Khan AM, El-Daly H, Simmons E, Rajpoot NM (2013) Hymap: a hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images. J Pathol Inform 4(2):1","journal-title":"J Pathol Inform"},{"key":"1532_CR33","doi-asserted-by":"crossref","unstructured":"Punitha S, Amuthan A, Joseph SK (2018) Benign and malignant breast cancer segmentation using optimized region growing technique. Futur Comput Inform J 3(2):348\u2013358","DOI":"10.1016\/j.fcij.2018.10.005"},{"key":"1532_CR34","doi-asserted-by":"crossref","unstructured":"Wang J, Wang Y, Tao X, Li Q, Sun L, Chen J, Zhou M, Menghan H, Zhou X (2021) Pca-u-net based breast cancer nest segmentation from microarray hyperspectral images. Fund Res 1(5):631\u2013640","DOI":"10.1016\/j.fmre.2021.06.013"},{"key":"1532_CR35","doi-asserted-by":"crossref","unstructured":"Rahman M, Hussain M\u00a0G, Hasan M\u00a0R, Babe S, Akter S (2020) Detection and segmentation of breast tumor from mri images using image processing techniques. In 2020 fourth international conference on computing methodologies and communication (ICCMC), pages 20\u2013724. IEEE,","DOI":"10.1109\/ICCMC48092.2020.ICCMC-000134"},{"key":"1532_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027","volume":"61","author":"M Byra","year":"2020","unstructured":"Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O\u2019Boyle M, Comstock C, Andre M (2020) Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed Signal Process Control 61:102027","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"1532_CR37","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.bbe.2021.05.007","volume":"41","author":"IA Enitan","year":"2021","unstructured":"Enitan IA, Utairat C, Makhanov Stanislav S (2021) A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning. Biocybern Biomed Eng 41(2):802\u2013818","journal-title":"Biocybern Biomed Eng"},{"key":"1532_CR38","doi-asserted-by":"publisher","unstructured":"Myvizhi M, Ali Ahmed (2023) Sustainable supply chain management in the age of machine intelligence: Addressing challenges, capitalizing on opportunities, and shaping the future landscape. Sustain Mach Intell J https:\/\/doi.org\/10.61185\/SMIJ.2023.33103","DOI":"10.61185\/SMIJ.2023.33103"},{"key":"1532_CR39","doi-asserted-by":"crossref","unstructured":"Nabeeh N (2023) Assessment and contrast the sustainable growth of various road transport systems using intelligent neutrosophic multi-criteria decision-making model","DOI":"10.61185\/SMIJ.2023.22102"},{"key":"1532_CR40","doi-asserted-by":"crossref","unstructured":"Sallam K, Mohamed M, Mohamed A\u00a0W (2023) Internet of things (iot) in supply chain management: challenges, opportunities, and best practices","DOI":"10.61185\/SMIJ.2023.22103"},{"key":"1532_CR41","doi-asserted-by":"publisher","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"S Li","year":"2019","unstructured":"Li S, Margolies Laurie R, Rothstein Joseph H, Eugene F, Russell M, Weiva S (2019) Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9:12495. https:\/\/doi.org\/10.1038\/s41598-019-48995-4","journal-title":"Sci Rep"},{"issue":"1","key":"1532_CR42","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1002\/ima.22516","volume":"31","author":"N Ravitha Rajalakshmi","year":"2021","unstructured":"Ravitha Rajalakshmi N, Vidhyapriya R, Elango N, Nikhil R (2021) Deeply supervised u-net for mass segmentation in digital mammograms. Int J Imaging Syst Technol 31(1):59\u201371. https:\/\/doi.org\/10.1002\/ima.22516","journal-title":"Int J Imaging Syst Technol"},{"key":"1532_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.177","author":"LR Sawyer","year":"2017","unstructured":"Sawyer LR, Francisco G, Assaf H, Kawai MK, Mia G, Rubin Daniel L (2017) Data descriptor: a curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. https:\/\/doi.org\/10.1038\/sdata.2017.177","journal-title":"Sci Data"},{"key":"1532_CR44","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"C Kenneth","year":"2013","unstructured":"Kenneth C, Bruce V, Kirk S, John F, Justin K, Paul Koppel, Stephen M, Stanley P, David M, Michael P, Lawrence T, Fred P (2013) The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digit Imaging 26:1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J Digit Imaging"},{"key":"1532_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105928","volume":"31","author":"H Mei-Ling","year":"2020","unstructured":"Mei-Ling H, Ting-Yu L (2020) Dataset of breast mammography images with masses. Data Brief 31:105928. https:\/\/doi.org\/10.1016\/j.dib.2020.105928","journal-title":"Data Brief"},{"issue":"2","key":"1532_CR46","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"C Moreira In\u00eas","year":"2012","unstructured":"Moreira In\u00eas C, In\u00eas DIAl, Ant\u00f3nio C, Jo\u00e3o CM, Cardoso Jaime S (2012) Inbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236\u2013248. https:\/\/doi.org\/10.1016\/j.acra.2011.09.014","journal-title":"Acad Radiol"},{"key":"1532_CR47","doi-asserted-by":"publisher","unstructured":"Li CH, Tam PKS (1998) An iterative algorithm for minimum cross entropy thresholding. Pattern Recog Lett 19(8):771\u2013776. https:\/\/doi.org\/10.1016\/S0167-8655(98)00057-9","DOI":"10.1016\/S0167-8655(98)00057-9"},{"key":"1532_CR48","doi-asserted-by":"crossref","unstructured":"Weaver JR (1985) Centrosymmetric (cross-symmetric) matrices, their basic properties, eigenvalues, and eigenvectors. Am Math Monthly 92(10):711\u2013717","DOI":"10.1080\/00029890.1985.11971719"},{"key":"1532_CR49","volume":"12","author":"U Kuran","year":"2021","unstructured":"Kuran U, Kuran EC (2021) Parameter selection for clahe using multi-objective cuckoo search algorithm for image contrast enhancement. Intell Syst Appl 12:200051","journal-title":"Intell Syst Appl"},{"key":"1532_CR50","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234\u2013241. Springer,","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1532_CR51","unstructured":"Kingma D\u00a0P, Ba J (2014) Adam: A method for stochastic optimization. dec. arxiv. org"},{"key":"1532_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112855","volume":"139","author":"SV Kumar","year":"2020","unstructured":"Kumar SV, Rashwan Hatem A, Santiago R, Farhan A, Nidhi P, Mostafa SMd, Kamal SA, Meritxell A, Miguel A, Domenec P et al (2020) Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl 139:112855","journal-title":"Expert Syst Appl"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01532-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01532-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01532-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T15:24:29Z","timestamp":1726327469000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01532-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,11]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1532"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01532-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,11]]},"assertion":[{"value":"20 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}