Original scientific paper

https://doi.org/10.31803/tg-20191023102807

Implementation of intelligent model for pneumonia detection

Željko Knok* orcid.org/0000-0003-0289-0994 ; Polytechnic of Međimurje in Čakovec, Bana Josipa Jelačica 22a, 40000 Čakovec, Croatia
Klaudio Pap ; University of Zagreb, Faculty of Graphic Arts, Getaldićeva 2, 10000 Zagreb, Croatia
Marko Hrnčić ; Zagreb University of Applied Sciences, Mlinarska cesta 38, 10000 Zagreb, Croatia

Abstract

The advancement of technology in the field of artificial intelligence and neural networks allows us to improve speed and efficiency in the diagnosis of various types of problems. In the last few years, the rise in the field of convolutional neural networks has been particularly noticeable, showing promising results in problems related to image processing and computer vision. Given that humans have limited ability to detect patterns in individual images, accurate diagnosis can be a problem for even medical professionals. In order to minimize the number of errors and unintended consequences, computer programs based on neural networks and deep learning principles are increasingly used as assistant tools in medicine. The aim of this study was to develop a model of an intelligent system that receives x-ray image of the lungs as an input parameter and, based on the processed image, returns the possibility of pneumonia as an output. The implementation of this functionality was implemented through transfer learning methodology based on already defined convolution neural network architectures.

Keywords

computer vision; machine learning; neural networks; pneumonia

Hrčak ID:

229501

URI

https://hrcak.srce.hr/229501

Publication date:

11.12.2019.

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