ǂA ǂnovel architecture to classify histopathology images using convolutional neural networks

Ibrahem Kandel (Author), Mauro Castelli (Author)

Abstract

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%.

Keywords

No keyword data available

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UL EF - Faculty of Economics
UDC:	004:78
COBISS:	38419459
ISSN:	2076-3417
Parent publication:	Applied sciences
Views:	373
Downloads:	98
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Type (COBISS):	Article
Pages:	str. 1-17
Volume:	ǂVol. ǂ10
Issue:	ǂiss. ǂ8 (art. 2929)
Chronology:	2020
DOI:	10.3390/app10082929
ID:	12365827