Differential Diagnosis of Benign and Malignant Ovarian Tumors Based on Convolutional Neural Network

Zhixin YANG; Lizhu ZHAO; Yue DENG; Lihua YANG

doi:10.12259/j.issn.2095-610X.S20231027

Volume 44 Issue 10

Oct. 2023

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Article Navigation > Journal of Kunming Medical University > 2023 > 44(10): 134-139

Zhixin YANG, Lizhu ZHAO, Yue DENG, Lihua YANG. Differential Diagnosis of Benign and Malignant Ovarian Tumors Based on Convolutional Neural Network[J]. Journal of Kunming Medical University, 2023, 44(10): 134-139. doi: 10.12259/j.issn.2095-610X.S20231027

Citation:

Zhixin YANG, Lizhu ZHAO, Yue DENG, Lihua YANG. Differential Diagnosis of Benign and Malignant Ovarian Tumors Based on Convolutional Neural Network[J]. Journal of Kunming Medical University, 2023, 44(10): 134-139. doi: 10.12259/j.issn.2095-610X.S20231027

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Differential Diagnosis of Benign and Malignant Ovarian Tumors Based on Convolutional Neural Network

doi: 10.12259/j.issn.2095-610X.S20231027

Dept. of Gynecology，The 2nd Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650101，China

Received Date: 2023-08-08
Available Online: 2023-09-12
Publish Date: 2023-10-25

Abstract

Abstract

Objective To establish a convolutional neural network model for ultrasound diagnosis of ovarian tumors and explore its value in distinguishing between benign and malignant ovarian tumors. Methods A total of 400 ovarian tumor ultrasound images, including 200 benign and 200 malignant tumors, were collected from June 2015 to September 2022 at the Second Affiliated Hospital of Kunming Medical University. These images were confirmed by cytology or histopathology. The images were divided into a training set and a validation set in a 1∶3 ratio. Two diagnostic models, VGG16 and MobileNet-V2 were constructed based on convolutional neural networks for training and validation. A senior and a junior sonographers were selected to diagnose the ultrasound images in the training set. The performance of the two diagnostic models and the ultrasound doctors in distinguishing between benign and malignant ovarian tumors was evaluated using the pathological results as the gold standard. Results The sensitivity, specificity and accuracy of VGG16 model in diagnosing the benign and malignant nature of ovarian tumors were 80.67%, 79.33% and 80.00% respectively. The sensitivity, specificity and accuracy of MobileNet-V2 were 89.33%, 93.33% and 91.33% respectively. The MobileNet-V2 model had the best diagnostic performance, and both the MobileNet-V2 and VGG16 models had better diagnostic performance than ultrasound doctors （P < 0.05）. Conclusion The convolutional neural network ovarian tumor diagnostic model has good diagnostic value, with the MobileNet-V2 model accurately determining the benign or malignant nature of ovarian tumor ultrasound images.
- Convolutional neural network,
- Ovarian tumors,
- Ultrasonic diagnosis

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References(20)

References

[1]	狄文,胡媛. 卵巢癌的大数据研究[J]. 中国实用妇科与产科杂志,2018,34(1):18-22. doi: 10.19538/j.fk2018010105
[2]	Reid F,Bhatla N,Oza A M,et al. The World Ovarian Cancer Coalition Every Woman Study: Identifying challenges and opportunities to improve survival and quality of life[J]. Int J Gynecol Cancer,2021,31(2):238-244. doi: 10.1136/ijgc-2019-000983
[3]	La Vecchia C. Ovarian cancer: Epidemiology and risk factors[J]. Eur J Cancer Prev,2017,26(1):55-62. doi: 10.1097/CEJ.0000000000000217
[4]	张家雨,何昕晖,宫婷婷,等. 肉类摄入与卵巢癌发病及预后关系的研究进展[J]. 肿瘤防治研究,2019,46(5):490-496.
[5]	Trinidad C V,Tetlow A L,Bantis L E,et al. Reducing ovarian cancer mortality through early detection: Approaches using circulating biomarkers[J]. Cancer Prev Res (Phila),2020,13(3):241-252. doi: 10.1158/1940-6207.CAPR-19-0184
[6]	Kim G R,Lee E,Kim H R,et al. Convolutional neural network to stratify the malignancy risk of thyroid nodules: Diagnostic performance compared with the American college of radiology thyroid imaging reporting and data system implemented by experienced radiologists[J]. AJNR Am J Neuroradiol,2021,42(8):1513-1519. doi: 10.3174/ajnr.A7149
[7]	Hejduk P,Marcon M,Unkelbach J,et al. Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network[J]. Eur Radiol,2022,32(7):4868-4878. doi: 10.1007/s00330-022-08558-0
[8]	Mitrea D,Badea R,Mitrea P,et al. Hepatocellular carcinoma automatic diagnosis within CEUS and B-Mode ultrasound images using advanced machine learning methods[J]. Sensors (Basel),2021,21(6):1-31. doi: 10.1109/JSEN.2021.3057283
[9]	李睿,许祥丛,林静怡,等. 基于卷积神经网络的超声影像肝癌自动分类[J]. 生物化学与生物物理进展,2023,50(3):668-675. doi: 10.16476/j.pibb.2022.0101
[10]	曹宇,邢素霞,逄键梁,等. 基于改进的VGG-16卷积神经网络的肺结节检测[J]. 中国医学物理学杂志,2020,37(7):940-944. doi: 10.3969/j.issn.1005-202X.2020.07.026
[11]	高慧明. 基于卷积神经网络的肺结节检测及良恶性分类方法研究[D]. 太原: 太原理工大学, 2019.
[12]	Han Y,Ma Y,Wu Z,et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images[J]. Eur J Nucl Med Mol Imaging,2021,48(2):350-360. doi: 10.1007/s00259-020-04771-5
[13]	Alshammari A. Construction of VGG16 convolution neural network (VGG16_CNN) classifier with NestNet-Based segmentation paradigm for brain metastasis classification[J]. Sensors (Basel),2022,22(20):1-19. doi: 10.1109/JSEN.2022.3211123
[14]	Altun S,Alkan A,Altun I. LSS-VGG16: Diagnosis of lumbar spinal stenosis with deep learning[J]. Clin Spine Surg,2023,36(5):E180-E190. doi: 10.1097/BSD.0000000000001418
[15]	刘雪纯,刘大铭,常佳鑫,等. 基于MobileNet V2迁移学习的中药材图像识别[J]. 长江信息通信,2022,35(7):33-37. doi: 10.3969/j.issn.1673-1131.2022.07.009
[16]	易振通,吴瑰,官端正,等. 轻量化卷积神经网络的研究综述[J]. 工业控制计算机,2022,35(10):109-111.
[17]	Palczynski K,Smigiel S,Gackowska M,et al. IoT application of transfer learning in hybrid artificial intelligence systems for acute lymphoblastic leukemia classification[J]. Sensors (Basel),2021,21(23):1-12. doi: 10.1109/JSEN.2021.3128120
[18]	Srinivasu P N,SivaSai J G,Ijaz M F,et al. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM[J]. Sensors (Basel),2021,21(8):1-27. doi: 10.1109/JSEN.2021.3063942
[19]	Yu C J,Yeh H J,Chang C C,et al. Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound[J]. Comput Methods Programs Biomed,2021,211:106382. doi: 10.1016/j.cmpb.2021.106382
[20]	Martinez-Mas J,Bueno-Crespo A,Khazendar S,et al. Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images[J]. PLoS One,2019,14(7):e219388.

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