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Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features

Kupas, Dávid and Harangi, Balázs (2022) Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features. In: 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022. július 11-15., Glasgow, United Kingdom.

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Abstract

The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features.

Item Type: Conference or Workshop Item (Speech)
Subjects: Q Science / természettudomány > Q1 Science (General) / természettudomány általában
Depositing User: Dr Balazs Harangi
Date Deposited: 29 Sep 2022 08:13
Last Modified: 29 Sep 2022 08:13
URI: http://real.mtak.hu/id/eprint/150456

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