| Title | Klasifikacija slika metodama dubokog učenja |
| Author | Filip Novoselnik |
| Mentor(s) | Ratko Grbić (thesis advisor)
|
| Abstract | Područje dubokih neuronskih mreža doživljava nagli uzlet u posljednjih nekoliko godina te postiže izvrsne rezultate u području obrade slike i računalnog vida. Diferencijalna krvna slika, tj. analiza bijelih krvnih stanica je jedno od zanimljivih područja primjene ovakvih metoda budući da se trenutno radi vizualnom inspekcijom krvnih preparata što je vremenski zahtjevno ili vrlo skupom i sofisticiranom opremom. U sklopu ovog rada predložen je algoritam za detekciju i identifikaciju bijelih krvnih stanica na krvnim razmazima koji se dobivaju kamerom i mikroskopom. Prva faza obrade je detekcija bijelih krvnih stanica segmentacijom slike kojom se nastoje izdvojiti sve bijele krvne stanice. U drugoj fazi je treniran model konvolucijske neuronske mreže na temelju 168 označenih slika dobivenih iz Kliničkog Bolničkog Centra Osijek. Implementirani model klasificira stanice u 5 klasa: eozinofili, limfociti, monociti, segmentirani neutrofili i nepoznato, tj. slučaj kada je došlo do pogreške segmentacije. Točnost segmentacije je blizu 90 %, dok je točnost klasifikacije oko 81 %. Predloženi algoritam dao je vrlo dobre rezultate te demonstrirao potencijal korištenih metoda koji bi u potpunosti bio iskorišten kada bi model bio treniran na puno većem broju slika. |
| Keywords | deep learning convolutional neural networks image segmentation white blood cells classification computer vision |
| Parallel title (English) | Image classification using deep learning |
| Committee Members | Ratko Grbić (committee chairperson)
Robert Cupec (committee member)
Irena Galić (committee member)
|
| Granter | Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek |
| Lower level organizational units | Department of Computer Engineering and Automation
Chair of Automation and Robotics |
| Place | Osijek |
| State | Croatia |
| Scientific field, discipline, subdiscipline | TECHNICAL SCIENCES
Computing
Artificial Intelligence
|
| Study programme type | university |
| Study level | graduate |
| Study programme | Graduate University Study Programme in Computer Engineering |
| Academic title abbreviation | mag.ing.comp. |
| Genre | master's thesis |
| Language | Croatian |
| Defense date | 2017-12-14 |
| Parallel abstract (English) | During the last few years, area of deep learning is growing and advancing rapidly, achieving very good results in image processing and computer vision. Differential blood count (white blood cells count) is one of the areas where such methods could be applied. Currently, it is done by visual inspection which is very time consuming or by using very expansive and sophisticated equipment. In this paper, a new system for white blood cells detection and identification is described and implemented. The first step is image segmentation, which extracts single cells out of original image. The second step is deep convolutional neural network training based on 168 labeled images from Clinical Hospital Center Osijek. Implemented model classifies white blood cells into five classes: eosinophiles, lymphocytes, monocytes, neutrphiles or unknown object (handles the case when segmentation has failed). Accuracy of segmentation on test samples is around 90 %, while accuracy of classification on test samples is around 81 %. Taking into account relatively small number of training samples implemented system showed very good results. It has demonstrated the potential of convolutional neural networks that would be fullfilled on much larger image dataset. |
| Parallel keywords (Croatian) | duboko učenje konvolucijske neuronske mreže segmentacija slika klasifikacija bijelih krvnih stanica računalni vid |
| Resource type | text |
| Access condition | Open access |
| Terms of use |  |
| URN:NBN | https://urn.nsk.hr/urn:nbn:hr:200:527518 |
| Repository | https://repozitorij.etfos.hr |
