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

Vasile, Corina Maria; Udriștoiu, Anca Loredana; Ghenea, Alice Elena; Popescu, Mihaela; Gheonea, Cristian; Niculescu, Carmen Elena; Ungureanu, Anca Marilena; Udriștoiu, Ștefan; Drocaş, Andrei Ioan; Gruionu, Lucian Gheorghe; Gruionu, Gabriel; Iacob, Andreea Valentina; Alexandru, Dragoş Ovidiu

doi:10.3390/medicina57040395

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

Corina Maria Vasile

^1,2,†

Anca Loredana Udriștoiu

^3,†

Alice Elena Ghenea

^4,*

Mihaela Popescu

^5,*,

Cristian Gheonea

⁶,

Carmen Elena Niculescu

⁶,

Anca Marilena Ungureanu

⁴,

Ștefan Udriștoiu

³,

Andrei Ioan Drocaş

⁷,

Lucian Gheorghe Gruionu

⁸,

Gabriel Gruionu

⁹

Andreea Valentina Iacob

³ and

Dragoş Ovidiu Alexandru

¹⁰

PhD School Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

Department of Pediatric Cardiology, County Clinical Emergency Hospital of Craiova, 200642 Craiova, Romania

Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania

⁴

Department of Bacteriology-Virology-Parasitology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

⁵

Department of Endocrinology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

⁶

Department of Pediatrics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

⁷

Department of Urology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

⁸

Faculty of Mechanics, University of Craiova, 200512 Craiova, Romania

⁹

Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA

¹⁰

Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania

Authors to whom correspondence should be addressed.

^†

These authors share first authorship, their contribution on study design and manuscript preparation was equal.

Academic Editors: Anca Oana Docea, Daniela Calina and Miquel Martorell

Medicina 2021, 57(4), 395; https://doi.org/10.3390/medicina57040395

Received: 16 March 2021 / Revised: 13 April 2021 / Accepted: 15 April 2021 / Published: 19 April 2021

(This article belongs to the Special Issue Modern Lifestyle, Anthropogens and Chronic Diseases from Myths to Reality)

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Abstract

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. View Full-Text

Keywords: thyroid disorders; ultrasound image; deep learning; neural networks

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MDPI and ACS Style

Vasile, C.M.; Udriștoiu, A.L.; Ghenea, A.E.; Popescu, M.; Gheonea, C.; Niculescu, C.E.; Ungureanu, A.M.; Udriștoiu, Ș.; Drocaş, A.I.; Gruionu, L.G.; Gruionu, G.; Iacob, A.V.; Alexandru, D.O. Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods. Medicina 2021, 57, 395. https://doi.org/10.3390/medicina57040395

AMA Style

Vasile CM, Udriștoiu AL, Ghenea AE, Popescu M, Gheonea C, Niculescu CE, Ungureanu AM, Udriștoiu Ș, Drocaş AI, Gruionu LG, Gruionu G, Iacob AV, Alexandru DO. Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods. Medicina. 2021; 57(4):395. https://doi.org/10.3390/medicina57040395

Chicago/Turabian Style

Vasile, Corina M., Anca L. Udriștoiu, Alice E. Ghenea, Mihaela Popescu, Cristian Gheonea, Carmen E. Niculescu, Anca M. Ungureanu, Ștefan Udriștoiu, Andrei I. Drocaş, Lucian G. Gruionu, Gabriel Gruionu, Andreea V. Iacob, and Dragoş O. Alexandru. 2021. "Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods" Medicina 57, no. 4: 395. https://doi.org/10.3390/medicina57040395

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