Thyroid Nodule Cell Classification in Cytology Images Using Transfer Learning Approach

Analysis of fine needle aspiration cytology FNAC slides of thyroid nodules is a very crucial test before the preoperative diagnosis of thyroid malignancy. Cytology slides may be composed of different types of cells. Differentiating between cancerous cells and healthy cells plays an important role in the treatment. However, the conventional visual inspection is very time consuming and the process might endure inaccuracies because of the subject-level assessment. To the best of our knowledge, no work has been done for the multi-class cell level classification of thyroid nodules. In this paper, we propose a method for classification of cytology images at the cell level by using fine-tuned VGG-19 and AlexNet models, exploiting the transfer learning approach to better fit the model for classification of our dataset. Model evaluations are done by calculating the precision, recall, F1-score, and accuracy. Although the data is highly imbalanced, but both model have shown very good performance by achieving an accuracy of 93.05% and 92.88% by VGG-19 and AlexNet respectively.