Abstract
The incidence of nervous system and soft tissue is getting higher, and nuclear magnetic resonance is the preferred method of examination and is widely used. The segmentation of brain magnetic resonance (MR) images is the key to the subsequent operations such as three-dimensional reconstruction, quantitative analysis of normal tissues and diseased tissues. The accuracy of image segmentation is critical in the doctor's assessment with the location, shape, and size of the lesion tissue, as well as the determination of the disease and the correct diagnosis plan. The results of this study indicate that the multi-scale convolutional neural network (MSCNN) model can segment brain tumors accurately and effectively. Through multi-scale input, this paper overcomes the need to select specific input scales according to the size of the tumor, accommodate more neighborhood information from various angles, and adapt to different tumor sizes, thus improving the segmentation accuracy of brain tumors. Based on the same accuracy, the segmentation speed is accelerated to ensure the real-time segmentation further. This method can effectively segment the brain lesion tissue in the nuclear magnetic resonance image, which improves the generalization ability. It can be used for identifying the brain lesion tissue of the nuclear magnetic resonance medical image.
Highlights
With the advancement of the times, various new technologies are constantly emerging, and image-processing technology is advancing with the times
In order to accelerate the convergence of the network, and considering the inconsistent voxel values of the magnetic resonance imaging (MRI) images in the dataset, the image is normalized by means of mean and standard deviation [40]
This paper proposes a medical image segmentation method based on improved convolutional neural networks (CNN)
Summary
With the advancement of the times, various new technologies are constantly emerging, and image-processing technology is advancing with the times. RELATED WORK In the past research, the human subjective consciousness to understand the image, and extract specific feature information, such as gray information, texture information and symmetry information to achieve brain tumor segmentation, the results can only be better for specific images. On the segmentation of MRI brain tumor images, CNN has a supervised learning method to extract different classification features for different patient difference information. The application of the classical twodimensional CNN model to MRI brain tumor segmentation has the following problems: Each modality of the 1MR image emphasizes different information, how to extract the difference information while removing redundant information, and achieve higher precision classification. Due to the variability of gray scale, texture, position, size, and shape of brain tumors, conventional CNN models still cannot achieve good segmentation To this end, this study proposes a MSCNN model. Through multi-scale input, it is necessary to select specific input scales according to the size of the tumor, to accommodate more neighborhood information from various angles, and to adapt to different tumor size, grayscale and texture changes, and thereby improving brain tumors
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