An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

• Published in: Computational and mathematical methods in medicine Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Yan, Hong, Zhao, Fanfan, Gu, Wen, Gao, Xiangyu, Du, Jiaoyang, Li, Daning, Wu, Wentao, Feng, Xiaojie
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi
• Subjects: Algorithms; Brain Neoplasms - diagnosis; Brain Neoplasms - diagnostic imaging; Computational Biology; Computer Simulation; Databases, Factual; Deep Learning; Glioma - diagnosis; Glioma - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image Interpretation, Computer-Assisted - statistics & numerical data; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Mathematical Concepts; Models, Neurological; Neural Networks, Computer; Neuroimaging - methods; Neuroimaging - statistics & numerical data; Support Vector Machine
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2020/6789306

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.