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A computer-aided diagnostic system for mammograms based on YOLOv3

  • 1182: Deep Processing of Multimedia Data
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Abstract

Due to a large amount of noise in medical images, the task of detecting and classifying the lesions of mammograms remains a huge challenge. Based on the existing deep learning methods, focusing on the diversity of breast cancer lesion types, this paper proposes a computer-aided diagnosis system based on YOLOv3 (You Only Look Once version 3) convolutional neural network for mammograms. In this system, we integrate detection and multi-classification problems of breast lesions into a regression problem, thereby simultaneously accomplish the two tasks in one framework. The proposed computer-aided diagnosis system is mainly divided into three components: preprocessing part of the original mammograms, deep convolutional neural network based on YOLOv3, processing and evaluation of the network output. We use the dataset from CBIS-DDSM to train three models: general model, mass model and microcalcification model. These trained models can detect the position of the input mammograms in different situations, and then classify them into mass, microcalcification, benign, malignant, and other categories. After evaluating the performance by using test set images, the accuracy rates of the general model, mass model, and microcalcification model trained by our system reach 93.667 %, 97.767 %, 96.870 % in the detection task, and 93.927 %, 98.121 %, 97.045 % in the classification task. The computer-aided diagnosis system performs well in lesion detection and classification tasks with high-noise mammograms, reflecting well robustness.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, Hubei, China

    Jianhui Zhao & Tianquan Chen

  2. School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, China

    Bo Cai

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  1. Jianhui Zhao
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  2. Tianquan Chen
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Correspondence to Bo Cai.

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Zhao, J., Chen, T. & Cai, B. A computer-aided diagnostic system for mammograms based on YOLOv3. Multimed Tools Appl 81, 19257–19281 (2022). https://doi.org/10.1007/s11042-021-10505-y

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