A novel architecture to classify histopathology images using convolutional neural networks
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author)

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Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.

Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication version:Version of Record
Number of pages:Str. 1-17
Numbering:Vol. 10, iss. 8 (art. 2929)
PID:20.500.12556/RUL-124233 This link opens in a new window
ISSN on article:2076-3417
DOI:10.3390/app10082929 This link opens in a new window
COBISS.SI-ID:38419459 This link opens in a new window
Publication date in RUL:11.01.2021
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
COBISS.SI-ID:522979353 This link opens in a new window


License:CC BY 4.0, Creative Commons Attribution 4.0 International
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:11.01.2021


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

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

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

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