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[115600] Artykuł:

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

Czasopismo: Sensors   Tom: 22, Zeszyt: 13, Strony: 4675
ISSN:  1424-8220
Opublikowano: Czerwiec 2022
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3		 Grupa
przynależności		 Dyscyplina
naukowa		 Procent
udziału		 Liczba
punktów
do oceny pracownika		 Liczba
punktów wg
kryteriów ewaluacji
Kian Ara Rouhollah Niespoza "N" jednostki017.00.00  
Andrzej Matiolański Niespoza "N" jednostki017.00.00  
Andrzej Dziech Niespoza "N" jednostki017.00.00  
Remigiusz Baran orcid logo WEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *Takzaliczony do "N"Automatyka, elektronika, elektrotechnika i technologie kosmiczne17100.00100.00  
Paweł Domin Niespoza "N" jednostki017.00.00  
Adam Wieczorkiewicz Niespoza "N" jednostki017.00.00  

Grupa MNiSW:  Publikacja w czasopismach wymienionych w wykazie ministra MNiSzW (część A)
Punkty MNiSW: 100

Pełny tekstPełny tekst     DOI LogoDOI    
Keywords:

artificial neural networks  biomedical imaging  image analysis  optical coherence tomography  oct  convolutional neural network 


Abstract:

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.


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