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Learning Multi-Level Features for Breast Mass Detection

Authors:

Qinggong Zeng,

Huiqin Jiang,

Ling MaAuthors Info & Claims

ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

Pages 16 - 20

https://doi.org/10.1145/3285996.3286000

Published: 13 October 2018 Publication History

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Abstract

In order to quickly detect masses from mammography images for the early screening of breast cancer, this paper proposes a breast mass detection improved algorithm based on Faster R-CNN. Firstly, we connect multi- level feature maps (conv-4, conv-5) in ZF model to generated candidate regions in RPN, then use the ROI pool layer to extract the features of the candidate regions. Finally the full connection layer output the region's classification score and the bounding box after regression. Experiments show that the detection sensitivity of this model for breast masses is 93.6%, and the average number of false positives per image is reduced to 0.651. Compared with the original model, the sensitivity of this one increases by 8.5 percentage points and its performance is excellent in the detection of breast masses.

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An JYu HBai RLi JWang YCao R(2022)Detection and Segmentation of Breast Masses Based on Multi-Layer Feature FusionMethods10.1016/j.ymeth.2021.04.022202(54-61)Online publication date: Jun-2022
https://doi.org/10.1016/j.ymeth.2021.04.022
Oza PSharma PPatel S(2022)A Drive Through Computer-Aided Diagnosis of Breast Cancer: A Comprehensive Study of Clinical and Technical AspectsRecent Innovations in Computing10.1007/978-981-16-8248-3_19(233-249)Online publication date: 10-Mar-2022
https://doi.org/10.1007/978-981-16-8248-3_19
Altan G(2020)A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous MassDeep Learning for Cancer Diagnosis10.1007/978-981-15-6321-8_10(169-187)Online publication date: 13-Sep-2020
https://doi.org/10.1007/978-981-15-6321-8_10

Index Terms

Learning Multi-Level Features for Breast Mass Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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A significant number of women are diagnosed with breast cancer each year. Early detection of breast masses is crucial in improving patient prognosis and survival rates. In recent years, deep learning techniques, particularly object detection ...
Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection
Abstract
Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most ...
Highlights
- A computer-aided mammographic mass detection is proposed.
- Tunicate swarm optimized hyperbolic tangent function for mammogram enhancement.
- A robust local binary pattern is explored for false positive reduction.
- Sensitivity of ...
Mass detection and classification in breast ultrasound images

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ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

October 2018

166 pages

ISBN:9781450365338

DOI:10.1145/3285996

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 the author(s) 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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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Published: 13 October 2018

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ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018

October 13 - 14, 2018

Chengdu, China

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An JYu HBai RLi JWang YCao R(2022)Detection and Segmentation of Breast Masses Based on Multi-Layer Feature FusionMethods10.1016/j.ymeth.2021.04.022202(54-61)Online publication date: Jun-2022
https://doi.org/10.1016/j.ymeth.2021.04.022
Oza PSharma PPatel S(2022)A Drive Through Computer-Aided Diagnosis of Breast Cancer: A Comprehensive Study of Clinical and Technical AspectsRecent Innovations in Computing10.1007/978-981-16-8248-3_19(233-249)Online publication date: 10-Mar-2022
https://doi.org/10.1007/978-981-16-8248-3_19
Altan G(2020)A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous MassDeep Learning for Cancer Diagnosis10.1007/978-981-15-6321-8_10(169-187)Online publication date: 13-Sep-2020
https://doi.org/10.1007/978-981-15-6321-8_10

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Breast Mass Detection and Classification Using Transfer Learning on OPTIMAM Dataset Through RadImageNet Weights

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Mass detection and classification in breast ultrasound images

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Breast Mass Detection and Classification Using Transfer Learning on OPTIMAM Dataset Through RadImageNet Weights

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Mass detection and classification in breast ultrasound images

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