Abstract
This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values P ≥ 0.05 . The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different P ≥ 0.05 . In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference P ≥ 0.05 . The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant P < 0.05 . In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.
Highlights
Ovarian tumor refers to tumor that occur on the ovaries and is one of the common genital tumors in women
100 patients with ovarian tumor were treated in the hospital from January 2017 to January 2019. e patients were all female, aged 22–45 years old. e patients included in the study had undergone pathological examinations and computed tomography (CT) imaging examinations before surgery. is study had been approved by the Ethics Committee of hospital. e patient and his family members had a more detailed understanding of the content and methods of the study, and they agreed to sign the relevant informed consent
It revealed that the segmentation and reconstruction of the algorithm in this study showed higher definition and lower noise, and the overall presentation quality was better than that of the SE-Res Block U-shaped convolutional neural networks (CNN) algorithm and the density peak clustering algorithm, which were consistent with the above quantitative results
Summary
Ovarian tumor refers to tumor that occur on the ovaries and is one of the common genital tumors in women. Ovarian tumors have a high incidence in nonbirth women, early menarche or late menopause [1], and the incidence of women decreases with the increase in the number of childbirths. Ovarian tumor can be divided into two types: benign ovarian tumor and evil ovarian tumor [2]. Benign ovarian tumors account for about 75% of ovarian tumors; most of them are cystic, with uniform density, clear borders, smooth surfaces, cyst walls, and thin separation rules [3] and no wall nodules; 85%–90% of ovarian tumors have various types and are generally solid or cystic, with uneven density distribution [4]. Clinical imaging methods for breast tumors usually include magnetic resonance (MRI), ultrasound, and computed tomography (CT) [5].
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