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Fusing fine-tuned deep features for recognizing different tympanic membranes

Cömert Zafer

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

40--51

EN

Otitis media (OM) refers to a group of inflammatory diseases regarding the middle ear. Although there are a wide variety of disease types regarding OM, the most commonly seen disorders are acute otitis media (AOM), otitis media with effusion (OME) and chronic suppurative otitis media (CSOM). The examination of OM in the clinics is realized subjec-tively. This subjective examination is error-prone and leads to a limited variability among specialist. For these reasons, computer-aided systems are in demand. In this study, we focus on recognizing normal, AOM, CSOM, and earwax tympanic membrane (TM) conditions using fused fine-tuned deep features provided by pre-trained deep convolutional neural networks (DCNNs). These features are applied as the input to several networks, such as an artificial neural network (ANN), k-nearest neighbor (k NN), decision tree (DT) and support vector machine (SVM). Moreover, we release a new publicly available TM data set consisting of totally 956 otoscope images. As a result, the DCNNs yielded promising results. Especially, the most efficient results were provided by VGG-16 with an accuracy of 93.05 %. The fused fine-tuned deep features improved the overall classification success. Finally, the proposed model yielded promising results with an accuracy of 99.47 %, sensitivity of 99.35 %, and specificity of 99.77 % using the combination of the fused fine-tuned deep features and SVM model. Consequently, this study shows that fused fine-tuned deep features are rather useful in recognizing different TMs and these features can provide a fully automated model with high sensitivity.

2

Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images

Raghavendra U., Bhandary S. V., Gudigar A., Acharya U. R.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

170--180

EN

Glaucoma is the prime cause of blindness and early detection of it may prevent patients from vision loss. An expert system plays a vital role in glaucoma screening, which assist the ophthalmologists to make accurate decision. This paper proposes a novel technique for glaucoma detection using optic disk localization and non-parametric GIST descriptor. The method proposes a novel area based optic disk segmentation followed by the Radon transformation (RT). The change in the illumination levels of Radon transformed image are compensated using modified census transformation (MCT). The MCT images are then subjected to GIST descriptor to extract the spatial envelope energy spectrum. The obtained dimension of the GIST descriptor is reduced using locality sensitive discriminant analysis (LSDA) followed by various feature selection and ranking schemes. The ranked features are used to build an efficient classifier to detect glaucoma. Our system yielded a maximum accuracy (97.00%), sensitivity (97.80%) and specificity (95.80%) using support vector machine (SVM) classifier with nineteen features. Developed expert system also achieved maximum accuracy (93.62%), sensitivity (87.50%) and specificity (98.43%) for public dataset using twenty six features. The proposed method is efficient and computationally less expensive as it require only nineteen features to model a classifier for the huge dataset. Therefore the proposed method can be effectively utilized in hospitals for glaucoma screening.

3

Komputerowe wspomaganie diagnostyki układów hydraulicznych

Trela Z.

Logistyka

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2010

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nr 6

PL

W artykule przedstawiono postęp w stosowaniu komputerowego wspomagania procesów diagnostycznych układów hydraulicznych. Dokonano analizy narzędzi diagnostycznych w zarysie historycznym począwszy od najprostszych metod gdzie komputerowe wspomaganie ogranicza sie jedynie do obróbki ręcznie wprowadzonych do komputera wyników badań aż do w pełni zautomatyzowanego systemu diagnostyki układów hydraulicznych zbudowanego w Politechnice Radomskiej, w którym praktycznie cały proces realizowany jest przez system komputerowy zaprogramowany wg. odpowiednich algorytmów diagnostycznych.

EN

In this paper the development of computer-assisted systems used in the diagnosis of hydraulic components is presented. Analysis of diagnostic tools, from historical point of view is also shown. At first the simplest computer diagnostic systems in case of which all data have to be manually put into computer are discussed. Then, fully automated diagnostic systems programmed according to appropriate diagnostic algorithms and built in technical university of radom are described.