• Corina Maria Vasile, Anca Loredana Udriștoiu, Alice Elena Ghenea, Mihaela Popescu, Cristian Gheonea, Carmen Elena Niculescu, Anca Marilena Ungureanu, Ștefan Udriștoiu, Andrei Ioan Drocaş, Lucian Gheorghe Gruionu, Gabriel Gruionu, Andreea Valentina Iacob, and Dragoş Ovidiu Alexandru.
• PhD School Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
• Medicina (Kaunas). 2021 Apr 19; 57 (4).

AbstractBackground 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.

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