Research on breast ultrasound image segmentation method based on deep learning
Authors: 
Xiaofeng Song, Yu Zhao, Ke Yi, Yuejin ZhangAuthors Info & Claims
Xiaofeng Song, Yu Zhao, Ke Yi, Yuejin ZhangAuthors Info & ClaimsPages 189 - 194
Abstract
In the medical image segmentation task, many studies choose to increase the network depth to achieve higher detection accuracy, but the network parameters of the deep convolutional neural network are too large, and the number of medical images is very complex, leading to a long segmentation time. Therefore, to solve the above problems, an improved U-SegNet network was proposed to segment breast tumors. The experimental results show that the improved U-SegNet reduces the experimental parameters while ensuring the accuracy. Due to the different shapes of breast tumors and the unclear boundary of ultrasonic image shapes of malignant tumors, the segmentation accuracy of malignant tumors is less than that of benign tumors in the task of medical image segmentation. A new segmented network, an optimized network based on RefineNet, is proposed, which uses two different scale inputs sent to ResNet101 and ResNet34 for downsampling to obtain feature maps with different resolutions. Finally, through experimental comparison, the experimental results show that the segmentation accuracy of this method is improved.
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August 2024
443 pages
ISBN:9798400710353
DOI:10.1145/3697467
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Published: 08 November 2024
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IoTML 2024
IoTML 2024: 2024 4th International Conference on Internet of Things and Machine Learning
August 9 - 11, 2024
Nanchang, China
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