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A Breast Ultrasound Tumor Detection Framework Using Convolutional Neural Networks

Published: 31 May 2022 Publication History

Abstract

Accurate and efficient breast cancer screening is of great significance to women's health. In order to solve the severe challenges in mass breast screening, such as poor ultrasound image quality, differences in the age and geographical distribution of the population, we proposed a detection framework based on convolution neural networks for tumor detection and tracking in ultrasound video. Firstly, some data pre-processing and tricks are adjust to improve YOLOv4 for making it more suitable for tumor detection task. Secondly, Kernelized Correlation Filters (KCF) tracking algorithm as post-processing is used to track and fuse all the detection bounding boxes. In this way, all the detection results can be aggregated to form a smaller number of tumor sequences, and some false positives can also be filtered out. The proposed method was evaluated on 251 cases with tumors. It obtains a promising result with sensitivity 97.62% and 12.3 false positives per case. Experimental results demonstrate that our method has better performance on tumor detection for ultrasound videos from mass breast screening.

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Cited By

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  • (2025)Joint Lesion Detection and Classification of Breast Ultrasound Video via a Clinical Knowledge-Aware FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.345249735:1(45-61)Online publication date: Jan-2025
  • (2024)Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor TargetsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326284421:3(2478-2493)Online publication date: Jul-2024
  • (2023)A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical ImagingRAiSE-202310.3390/engproc2023059003(3)Online publication date: 11-Dec-2023
  • Show More Cited By

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cover image ACM Other conferences
BIC '22: Proceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing
January 2022
551 pages
ISBN:9781450395755
DOI:10.1145/3523286
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2022

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Author Tags

  1. Breast ultrasound
  2. Tracking algorithm
  3. Tumor detection
  4. YOLOv4

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Cited By

View all
  • (2025)Joint Lesion Detection and Classification of Breast Ultrasound Video via a Clinical Knowledge-Aware FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.345249735:1(45-61)Online publication date: Jan-2025
  • (2024)Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor TargetsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326284421:3(2478-2493)Online publication date: Jul-2024
  • (2023)A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical ImagingRAiSE-202310.3390/engproc2023059003(3)Online publication date: 11-Dec-2023
  • (2023)Evaluation of artificial intelligent breast ultrasound on lesion detection and characterization compared with hand-held ultrasound in asymptomatic womenFrontiers in Oncology10.3389/fonc.2023.120726013Online publication date: 15-Jun-2023

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