Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model

Title

Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model

Creator

Lijo J.; Saleema J.S.

Description

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.

Source

Journal of Advances in Information Technology, Vol-15, No. 10, pp. 1184-1192.

Date

2024-01-01

Publisher

Engineering and Technology Publishing

Subject

breast cancer; deep learning; histopathological images; mitosis cells; segmentation; YOLO-v5

Coverage

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

Rights

Restricted Access

Relation

ISSN: 17982340

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.12720/jait.15.10.1184-1192

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208477536&doi=10.12720%2fjait.15.10.1184-1192&partnerID=40&md5=28a7b6dfdb1433e0466a0d1284f38a42