Title:Application Exploration of Medical Image-aided Diagnosis of Breast Tumour
Based on Deep Learning
Volume: 20
Author(s): Zhen Hong*, Xin Yan, Ran Zhang, Yuanfang Ren, Qian Tong and Chadi Altrjman
Affiliation:
- School of Nursing, Jiangsu Health Vocational College, Nanjing 211800, Jiangsu, China
Keywords:
Image-aided diagnosis, Medical images, Breast tumour, Deep learning, Cancer diagnosis, MRI.
Abstract:
Background:
Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty
and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become
an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different
depths.
Objective:
This work aimed to use DL to study medical image-assisted diagnosis.
Methods:
The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated.
The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation
indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to
diagnose the medical image of breast tumour.
Results:
The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of
breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical
image-aided diagnosis.
Conclusion:
The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has
been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI
region extraction effect and breast tumor image diagnosis effect.
