Abstract
In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm (P < 0.05). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group (P < 0.05). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient's anxiety and ensure that high-quality MRI images were obtained after the examination.
Highlights
Brain tumor is a cell that grows abnormally in brain tissue, causing serious damage in the human body and posing a threat to the health of patients [1]
The magnetic resonance imaging (MRI) image characteristics of the brain tumor based on the convolutional neural network (CNN) algorithm were more accurate and clearer than the MRI image features of the conventional algorithm, which can accurately identify the location of the patient’s brain tumor
Such results indicated that the deep learning algorithm can accurately identify and segment brain tumors of various sizes and states and the corresponding brain lesions, and the image results of each mode were very close to the truth labels
Summary
Brain tumor is a cell that grows abnormally in brain tissue, causing serious damage in the human body and posing a threat to the health of patients [1]. Under the research and exploration of clinical tumor diagnosis and treatment technology, computerized tomography (CT), magnetic resonance imaging (MRI), and other imaging technologies have become the main force in medical image analysis. MRI imaging technology has become a common method for the diagnosis and treatment of brain tumors. The CNN algorithm has shown its characteristics beyond other algorithms in natural image segmentation, which brings a good opportunity for the diagnosis and feature analysis of brain tumor images [6]. Jeffrey’s study and other related research studies showed that deep learning algorithm technology showed excellent analysis performance on MRI images, which promoted the detection rate of breast cancer, brain tumor, and other malignant tumors [7]
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