Experience of applying convolutional neural network for binary classification of microphotographs of thyroid cytology specimens

Objective: to evaluate the effectiveness of a convolutional neural network model for automated cytologic diagnosis of papillary thyroid cancer and benign thyroid nodules. Material and Methods. The convolutional neural network was developed in the Python programming language using the TensorFlow 2.15.0 open source library. For the study, a dataset that included two categories of pathologies was generated: 1597 microphotographs of papillary carcinoma and 767 microphotographs of benign nodules (colloid goiter and adenomatous nodules). To form a training sample and evaluate the model’s performance metrics on the test sample, the dataset was divided in a ratio of 80/20.