Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification; [Un modelo marco progresivo UNDML para el diagntico y clasificaci del ccer de mama] Subject The topic of the resource Breast Cancer Detection; CNN; Feature Extraction; Mammograms; Preprocessing; Segmentation Description An account of the resource According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the diseases fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy. 2024; Los autores. Creator An entity primarily responsible for making the resource Meenalochini G.; Guka D.A.; Sivasakthivel R.; Rajagopal M. Source A related resource from which the described resource is derived Data and Metadata, Vol-3 Publisher An entity responsible for making the resource available Editorial Salud, Ciencia y Tecnologia Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.56294/dm2024198" target="_blank" rel="noreferrer noopener">https://doi.org/10.56294/dm2024198</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184859032&amp;doi=10.56294%2Fdm2024198&amp;partnerID=40&amp;md5=9183b43afb88749713dc4cb3808e6a52" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184859032&amp;doi=10.56294%2fdm2024198&amp;partnerID=40&amp;md5=9183b43afb88749713dc4cb3808e6a52</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 29534917 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Meenalochini G., School of Computing, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, India; Guka D.A., School of Computing, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Rajagopal M., Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India