scholarly journals Classification of down-core foraminifera image sets using convolutional neural networks.

2019 ◽  
Author(s):  
Ross Marchant ◽  
Martin Tetard ◽  
Adnya Pratiwi ◽  
Thibault de Garidel-Thoron
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
The North ◽  
Manual Counting ◽  
North Eastern ◽  
Long Time ◽  
Image Set

Manual identification of foraminifera species or morphotypes under stereoscopic microscopes is time-consuming for the taxonomist, and a long-time goal has been automating this process to improve efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large down-core foraminifera image set using convolutional neural networks. Construction of the classifier is demonstrated on the publically available Endless Forams image set with an best accuracy of approximately 90%. A complete down-core analysis is performed for benthic species in the Holocene period for core MD02-2518 from the North Eastern Pacific, and the relative abundances compare favourably with manual counting, showing the same signal dynamics. Using our workflow opens the way to automated paleo-reconstruction based on computer image analysis, and can be employed using our labelling and classification software, ParticleTrieur.

scholarly journals Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

2020 ◽  
Vol 39 (2) ◽  
pp. 183-202 ◽  
Author(s):  
Ross Marchant ◽  
Martin Tetard ◽  
Adnya Pratiwi ◽  
Michael Adebayo ◽  
Thibault de Garidel-Thoron
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Analysis ◽  
Warm Pool ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
The North ◽  
Manual Counting ◽  
Large Foraminifera ◽  
Fossil Records

Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.

scholarly journals Automated Classification of Bacterial Cell Sub-Populations with Convolutional Neural Networks

2020 ◽  
Author(s):  
Denis Tamiev ◽  
Paige Furman ◽  
Nigel Reuel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Bacterial Cell ◽  
Complete Solution ◽  
Training Data ◽  
Bacterial Cells ◽  
Automated Classification ◽  
The Neural Network ◽  
Manual Counting

AbstractQuantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-throughput, autonomous approaches. In this work, we apply classification-type convolutional neural networks (cCNN) to classify and enumerate bacterial cell sub-populations (B. subtilis clusters). Here, we demonstrate that the accuracy of the cCNN developed in this study can be as high as 86% when trained on a relatively small dataset (81 images). We also developed a new image preprocessing algorithm, specific to fluorescent microscope images, which increases the amount of training data available for the neural network by 72 times. By summing the classified cells together, the algorithm provides a total cell count which is on parity with manual counting, but is 10.2 times more consistent and 3.8 times faster. Finally, this work presents a complete solution framework for those wishing to learn and implement cCNN in their synthetic biology work.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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