Classification of thyroid diseases in OCT images using convolutional neural networks

Iulian E. Tampu; Anders Eklund; Kenth Johansson; Oliver Gimm; Neda Haj-Hosseini

doi:10.1117/12.2608110

9 March 2022 Classification of thyroid diseases in OCT images using convolutional neural networks

Iulian E. Tampu, Anders Eklund, Kenth Johansson, Oliver Gimm, Neda Haj-Hosseini

Proceedings Volume PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX; PC119490F (2022) https://doi.org/10.1117/12.2608110
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Optical coherence tomography (OCT) shows potential as an intraoperative guidance tool. However, OCT images are difficult to interpret and real-time analysis methods are needed to promote its clinical use. This study investigates deep learning-based OCT image classification with application on thyroid diseases. To evaluate the impact of data pre-processing and model architecture on classification performance, 2D and 3D deep learning models were implemented and trained on OCT data from ex-vivo thyroid samples. For 2D classification, deeper models and the ones using information from different spatial resolution achieved highest performance. However, 3D models outperform the 2D counterparts in most classification tasks.

Conference Presentation

Citation Download Citation

Iulian E. Tampu, Anders Eklund, Kenth Johansson, Oliver Gimm, and Neda Haj-Hosseini "Classification of thyroid diseases in OCT images using convolutional neural networks", Proc. SPIE PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, PC119490F (9 March 2022); https://doi.org/10.1117/12.2608110

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