Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods

被引:17
|
作者
Vasile, Corina Maria [1 ,2 ]
Udristoiu, Anca Loredana [3 ]
Ghenea, Alice Elena [4 ]
Popescu, Mihaela [5 ]
Gheonea, Cristian [6 ]
Niculescu, Carmen Elena [6 ]
Ungureanu, Anca Marilena [4 ]
Udristoiu, Stefan [3 ]
Drocas, Andrei Ioan [7 ]
Gruionu, Lucian Gheorghe [8 ]
Gruionu, Gabriel [9 ]
Iacob, Andreea Valentina [3 ]
Alexandru, Dragos Ovidiu [10 ]
机构
[1] Univ Med & Pharm Craiova, PhD Sch Dept, Craiova 200349, Romania
[2] Cty Clin Emergency Hosp Craiova, Dept Pediat Cardiol, Craiova 200642, Romania
[3] Univ Craiova, Fac Automat Comp & Elect, Craiova 200776, Romania
[4] Univ Med & Pharm Craiova, Dept Bacteriol Virol Parasitol, Craiova 200349, Romania
[5] Univ Med & Pharm Craiova, Dept Endocrinol, Craiova 200349, Romania
[6] Univ Med & Pharm Craiova, Dept Pediat, Craiova 200349, Romania
[7] Univ Med & Pharm Craiova, Dept Urol, Craiova 200349, Romania
[8] Univ Craiova, Fac Mech, Craiova 200512, Romania
[9] Indiana Univ Sch Med, Dept Med, Indianapolis, IN 46202 USA
[10] Univ Med & Pharm Craiova, Dept Med Informat & Biostat, Craiova 200349, Romania
来源
MEDICINA-LITHUANIA | 2021年 / 57卷 / 04期
关键词
thyroid disorders; ultrasound image; deep learning; neural networks; HASHIMOTOS-THYROIDITIS; NODULE; ULTRASONOGRAPHY; IMAGES;
D O I
10.3390/medicina57040395
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background and Objectives: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagnosis process is necessary. Materials and Methods: For this purpose, we developed an ensemble method that fused two deep learning models, one based on convolutional neural network and the other based on transfer learning. For the first model, called 5-CNN, we developed an efficient end-to-end trained model with five convolutional layers, while for the second model, the pre-trained VGG-19 architecture was repurposed, optimized and trained. We trained and validated our models using a dataset of ultrasound images consisting of four types of thyroidal images: autoimmune, nodular, micro-nodular, and normal. Results: Excellent results were obtained by the ensemble CNN-VGG method, which outperformed the 5-CNN and VGG-19 models: 97.35% for the overall test accuracy with an overall specificity of 98.43%, sensitivity of 95.75%, positive and negative predictive value of 95.41%, and 98.05%. The micro average areas under each receiver operating characteristic curves was 0.96. The results were also validated by two physicians: an endocrinologist and a pediatrician. Conclusions: We proposed a new deep learning study for classifying ultrasound thyroidal images to assist physicians in the diagnosis process.
引用
收藏
页数:14
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