Abstract
Over the past decade, improvements in image analysis methods and substantial advancements in the processing power have allowed the development of powerful computer-aided analytical approaches to medical data. Tissue histology slides can now be scanned and preserved in digital form, thanks to the recent introduction of entire slide digital scanners. In such a form, they can serve as input data for Artificial Intelligence (AI) algorithms that can speed up standard procedures for histology analysis with high accuracy and precision. This research aimed to create an automated system based on AI for histopathological image analysis. The first step was normalizing H&E-stain images and then using them as input to the convolutional neural network. The best results are achieved using ResNet50 with the highest AUC value of 0.98 (±σ = 0.02). Such an approach proved to be successful in analyzing histopathological images.
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Acknowledgments
This research has been (partly) supported by the CEEPUS network CIII-HR-0108, European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS), project CEKOM under the grant KK.01.2.2.03.0004, Erasmus+ project WICT under the grant 2021–1-HR01-KA220-HED-000031177 and University of Rijeka scientific grant uniri-tehnic-18–275-1447.
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Štifanic, J., Štifanić, D., Zulijani, A., Car, Z. (2023). Application of AI in Histopathological Image Analysis. In: Filipovic, N. (eds) Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering. AAI 2022. Lecture Notes in Networks and Systems, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-29717-5_9
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DOI: https://doi.org/10.1007/978-3-031-29717-5_9
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