MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

Bo Feng,Yanli Zheng,Lingang Wang,Hanlin Zhu,Meihua Zhang,Yuvaraja Teekaraman

doi:10.1155/2021/9668836

Bo Feng, Yanli Zheng + Show 4 more

Open Access

https://doi.org/10.1155/2021/9668836

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Abstract

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

Highlights

In China, lung cancer ranks first for the incidence and mortality among malignant tumors
The Mean Square Error (MSE) value of the SVMbased optimized Support Vector Machine (SVM)-L model was compared with that of fuzzy C-means (FCM), Convolutional Neural Network (CNN), SVM, local binary fitting (LBF), and Generative Adversarial Network (GAN) algorithms. e comparison result showed that, in 15 detected MRI images, the MSE value of SVM-L models was apparently lower than that of other algorithms and the average MSE value was 0.41 ± 0.02 (Figure 2)
E Dice index (DI) values of MRI images segmented through different algorithms were compared (Figure 3). e comparison result showed that the DI values of SVM-L models in 15 detected MRI images were apparently lower than those of other algorithms and the average DI value was 0.84 ± 0.13

Summary

Introduction

In China, lung cancer ranks first for the incidence and mortality among malignant tumors. Lung cancer has the highest mortality and its incidence ranks third. E CT image of SPN is of high density with the D ≤ 3 cm, distinct or indistinct borders, and round or irregular shapes. Single round or oval nodules in the lung parenchyma are not accompanied with pulmonary atelectasis and lymphadenectasis and the like [2]. E X-ray misses the hidden part or small lesions, and its detection rate of SPNs in adult lungs is only 0.1%–0.2% [4]. CTP and spectral CT technology are efficient in diagnosing pulmonary nodules, but they require a high radiation dose, which limits their clinical applications [5]

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