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2021 | OriginalPaper | Buchkapitel

Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks

verfasst von : Amirreza Mahbod, Gerald Schaefer, Rupert Ecker, Isabella Ellinger

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Pollen grain micrograph classification has multiple applications in medicine and biology. Automatic pollen grain image classification can alleviate the problems of manual categorisation such as subjectivity and time constraints. While a number of computer-based methods have been introduced in the literature to perform this task, classification performance needs to be improved for these methods to be useful in practice.

In this paper, we present an ensemble approach for pollen grain microscopic image classification into four categories: Corylus Avellana well-developed pollen grain, Corylus Avellana anomalous pollen grain, Alnus well-developed pollen grain, and non-pollen (debris) instances. In our approach, we develop a classification strategy that is based on fusion of four state-of-the-art fine-tuned convolutional neural networks, namely EfficientNetB0, EfficientNetB1, EfficientNetB2 and SeResNeXt-50 deep models. These models are trained with images of three fixed sizes (\(224 \times 224\), \(240 \times 240\), and \(260 \times 260\) pixels) and their prediction probability vectors are then fused in an ensemble method to form a final classification vector for a given pollen grain image.

Our proposed method is shown to yield excellent classification performance, obtaining an accuracy of 94.48% and a weighted F1-score of 94.54% on the ICPR 2020 Pollen Grain Classification Challenge training dataset based on five-fold cross-validation. Evaluated on the test set of the challenge, our approach achieves a very competitive performance in comparison to the top ranked approaches with an accuracy and weighted F1-score of 96.28% and 96.30%, respectively.

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Vorheriges Kapitel Organ Segmentation with Recursive Data Augmentation for Deep Models

Nächstes Kapitel Active Surface for Fully 3D Automatic Segmentation

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Titel: Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks
verfasst von: Amirreza Mahbod
Gerald Schaefer
Rupert Ecker
Isabella Ellinger
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68763-2_26

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