Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection-Reference-Cited by-同舟云学术

Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection

Published:2023-06-01 Issue: Volume: Page:
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Container-title:2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST)
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Author:

Intasam Anongnat¹,Promworn Yuttachon¹,Juhong Aniwat²,Thanasitthichai Somchai³,Khwayotha Sirihattaya³,Jiranantanakorn Titipon⁴,Thawornwanchai Soonthorn⁴,Piyawattanametha Wibool¹

Affiliation:

1. School of Engineering, King Mongkut's Institute of Technology Ladkrabang,Biomedical Engineering Department,Bangkok,Thailand,10520

2. Institute for Quantitative Health Sciences and Engineering, Michigan State University,East Lansing,MI,USA,48823

3. Udonthani Cancer Hospital,Undonthani,Thailand,41300

4. National Cancer Institute,Diagnostic Radiology and Nuclear Medicine Department,Bangkok,Thailand,10400

Funder

King Mongkut's Institute of Technology Ladkrabang Research Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10156867/10156897/10157611.pdf?arnumber=10157611

Reference11 articles.

1. Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

2. YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings

3. An overview of gradient descent optimization algorithms;ruder;ArXiv Preprint,2016

4. YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms

5. Ultra-high resolution, multi-scale, context-aware approach for detection of small cancers on mammography

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