A novel architecture to classify histopathology images using convolutional neural networks

Kandel, Ibrahem (avtor), Castelli, Mauro (avtor)

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%.

• Jezik: Angleški jezik
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: Str. 1-17
• Številčenje: Vol. 10, iss. 8 (art. 2929)
• PID: 20.500.12556/RUL-124233
• UDK: 004:78
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app10082929
• COBISS.SI-ID: 38419459
• Datum objave v RUL: 11.01.2021

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 11.01.2021