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Comparative study of first order optimizers for image classification using convolutional neural networks on histopathology images
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author), ID Popovič, Aleš (Author)

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Abstract
The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.

Language:English
Keywords:image classification, convolutional neural networks, deep learning, medical images, transfer learning, optimizers, neuroscience
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication status:Published
Publication version:Version of Record
Year:2020
Number of pages:17 str.
Numbering:Vol. 6, iss. 9, art. 92
PID:20.500.12556/RUL-120547 This link opens in a new window
UDC:659.2:004
ISSN on article:2313-433X
DOI:10.3390/jimaging6090092 This link opens in a new window
COBISS.SI-ID:27679235 This link opens in a new window
Publication date in RUL:22.09.2020
Views:869
Downloads:239
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Record is a part of a journal

Title:Journal of imaging
Shortened title:J. imaging
Publisher:MDPI
ISSN:2313-433X
COBISS.SI-ID:525653017 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:08.09.2020

Secondary language

Language:Slovenian
Keywords:informatika, programiranje, prenos znanja, kognitivna znanost

Projects

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0022/2018
Acronym:GADgET

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0113/2019
Acronym:AICE

Funder:ARRS - Slovenian Research Agency
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

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