Implementation of intelligent model for pneumonia detection
; Polytechnic of Međimurje in Čakovec, Bana Josipa Jelačica 22a, 40000 Čakovec, Croatia
; University of Zagreb, Faculty of Graphic Arts, Getaldićeva 2, 10000 Zagreb, Croatia
; Zagreb University of Applied Sciences, Mlinarska cesta 38, 10000 Zagreb, Croatia
APA 6th Edition Knok*, Ž., Pap, K. i Hrnčić, M. (2019). Implementation of intelligent model for pneumonia detection. Tehnički glasnik, 13 (4), 315-322. https://doi.org/10.31803/tg-20191023102807
MLA 8th Edition Knok*, Željko, et al. "Implementation of intelligent model for pneumonia detection." Tehnički glasnik, vol. 13, br. 4, 2019, str. 315-322. https://doi.org/10.31803/tg-20191023102807. Citirano 25.06.2021.
Chicago 17th Edition Knok*, Željko, Klaudio Pap i Marko Hrnčić. "Implementation of intelligent model for pneumonia detection." Tehnički glasnik 13, br. 4 (2019): 315-322. https://doi.org/10.31803/tg-20191023102807
Harvard Knok*, Ž., Pap, K., i Hrnčić, M. (2019). 'Implementation of intelligent model for pneumonia detection', Tehnički glasnik, 13(4), str. 315-322. https://doi.org/10.31803/tg-20191023102807
Vancouver Knok* Ž, Pap K, Hrnčić M. Implementation of intelligent model for pneumonia detection. Tehnički glasnik [Internet]. 2019 [pristupljeno 25.06.2021.];13(4):315-322. https://doi.org/10.31803/tg-20191023102807
IEEE Ž. Knok*, K. Pap i M. Hrnčić, "Implementation of intelligent model for pneumonia detection", Tehnički glasnik, vol.13, br. 4, str. 315-322, 2019. [Online]. https://doi.org/10.31803/tg-20191023102807
Sažetak 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.