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Convolutional neural networks for early computer diagnosis of child dysplasia

Bilynsky Yosyp, Nikolskyy Aleksandr, Revenok Viktor, Pogorilyi Vasyl, Smailova Saule, Voloshina Oksana, Kumargazhanova Saule

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

2023

T. 13, nr 2

56--63

The problem in ultrasound diagnostics hip dysplasia is the lack of experience of the doctor in case of incorrect orientation of the hip joint and ultrasound head. The aim of this study was to evaluate the ability of the convolutional neural network (CNN) to classify and recognize ultrasound imaging of the hip joint obtained at the correct and incorrect position of the ultrasound sensor head in the computer diagnosis of pediatric dysplasia. CNN's such as GoogleNet, SqueezeNet, and AlexNet were selected for the study. The most optimal for the task is the use of CNN GoogleNet showed. In this CNN used transfer learning. At the same time, fine-tuning of the network and additional training on the database of 97 standards of ultrasonic images of the hip joint were applied. Image type RGB 32 bit, 210 × 300 pixels are used. Fine-tuning has been performed the lower layers of the structure CNN, in which 5 classes are allocated, respectively 4 classes of hip dysplasia types according to the Graf, and the Type ERROR ultrasound image, where position of the ultrasound sensor head and of the hip joint in ultrasound diagnostics are incorrect orientation. It was found that the authenticity of training and testing is the highest for the GoogleNet network: when classified in the training group accuracy is up to 100%, when classified in the test group accuracy – 84.5%.

Problemem w diagnostyce ultrasonograficznej dysplazji stawu biodrowego jest brak doświadczenia lekarzy w zakresie nieprawidłowej orientacji stawu biodrowego i głowicy ultrasonograficznej. Celem tego badania była ocena zdolności konwolucyjnej sieci neuronowej (CNN) do klasyfikowania i rozpoznawania obrazów ultrasonograficznych stawu biodrowego uzyskanych przy prawidłowym i nieprawidłowym położeniu głowicy ultrasonograficznej we wspomaganej komputerowo diagnostyce dysplazji dziecięcej. Do badania wybrano sieci CNN, takie jak GoogleNet, SqueezeNet i AlexNet. Wykazano, że najbardziej optymalne dla tego zadania jest użycie CNN GoogleNet. Jednocześnie w CNN zastosowano metodologię uczenia transferowego. Zastosowano precyzyjne dostrojenie sieci i dodatkowe szkolenie na podstawie 97 próbek obrazów ultrasonograficznych stawu biodrowego, typ obrazu RGB 32 bity, 210 × 300 pikseli. Przeprowadzono dostrajanie dolnych warstw struktury CNN, w której zidentyfikowano 5 klas, odpowiednio 4 klasy typów dysplazji stawu biodrowego według Grafa oraz obraz ultrasonograficzny typu ERROR, w którym pozycja głowicy ultrasonograficznej i stawu biodrowego w diagnostyce ultrasonograficznej mają nieprawidłową orientację. Stwierdzono, że niezawodność szkolenia i testowania jest najwyższa dla sieci GoogleNet: podczas klasyfikacji w grupie szkoleniowej dokładność wynosi do 100%, podczas klasyfikacji w grupie testowej dokładność wynosi 84,5%.

Computer-aided method of diagnostics of gas turbine blades

Bogdan M., Błachnio J., Derlatka M.

Acta Mechanica et Automatica

2009

Vol. 3, no. 4

13-16

The article presents a computer-aided method of diagnostics of gas turbine blades with use of artificial neural networks. The subject of presentation is the developed neural network, with help of which – on the basis of features of blade surface images – realised is determination of their condition (operable element – inoperable element). Basing on conclusions formulated on the basis of microstructure examinations and concerning evaluation of state of overheating (blades suitable and not suitable for further operation), as patterns assumed were surface images representing blades in various states (neural pattern classification). Additionally, combining and segregating (according to their applicability for the network teaching process) image parameters, acquired from histograms as well as from matrix of events, automated and increased was the credibility (computer aiding) of decision process. The application of artificial neural network enables better representation of complex relations between blade image and its condition, than in the case of subjective methods used currently by diagnosticians.

Vibration syndrome diagnosis using cooling test verified by computerised photoplethysmography

Dyszkiewicz A., Tendera M.

Acta of Bioengineering and Biomechanics

2004

Vol. 6, nr 2

33-41

This study addresses itself to the problem of vibration syndrome diagnosis by means of photoplethysmography. The measurement was performed on a small area on the finger-tip plexus in which many arteriovenous anastomoses are present. In the opinion of many authors, flow disorders in this area are more typical of developing vibration syndrome than changes in the microvessels. Cooling test was then performed and it was verified both visually and using the computer method, allowing the functional measurement of the flow to be obtained. Computer method proved to be much more sensitive than traditional tests. The test proposed allows us to detect vascular disorders in the prodromal period and gives time for preventive measures to be taken.