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 Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model Subject The topic of the resource breast cancer; deep learning; histopathological images; mitosis cells; segmentation; YOLO-v5 Description An account of the resource Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors. Creator An entity primarily responsible for making the resource Lijo J.; Saleema J.S. Source A related resource from which the described resource is derived Journal of Advances in Information Technology, Vol-15, No. 10, pp. 1184-1192. Publisher An entity responsible for making the resource available Engineering and Technology Publishing 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/10.12720/jait.15.10.1184-1192" target="_blank" rel="noreferrer noopener">https://doi.org/10.12720/jait.15.10.1184-1192</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208477536&amp;doi=10.12720%2Fjait.15.10.1184-1192&amp;partnerID=40&amp;md5=28a7b6dfdb1433e0466a0d1284f38a42" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208477536&amp;doi=10.12720%2fjait.15.10.1184-1192&amp;partnerID=40&amp;md5=28a7b6dfdb1433e0466a0d1284f38a42</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17982340 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 Lijo J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India, School of Computer Applications, Dayananda Sagar University, Karnataka, Bengaluru, India; Saleema J.S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India