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Method-Induced Errors in Fractal Analysis of Lung Microscopic Images Segmented with the Use of HistAENN (Histogram-Based Autoencoder Neural Network)

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Department of Epidemiology and Management, Pomeranian Medical University, Zolnierska 48 St., 71210 Szczecin, Poland
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Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology Szczecin, 26. Kwietnia 10 St., 71126 Szczecin, Poland
3
Department of Forensic Medicine, Pomeranian Medical University, Powstancow Wielkopolskich 72 St., 70111 Szczecin, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2356; https://doi.org/10.3390/app8122356
Received: 1 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
The designing of Computer-Aided Diagnosis (CADx) is necessary to improve patient condition analysis and reduce human error. HistAENN (Histogram-based Autoencoder Neural Network, the first hierarchy level) and the fractal-based estimator (the second hierarchy level) are assumed for segmentation and image analysis, respectively. The aim of the study is to investigate how to select or preselect algorithms at the second hierarchy level algorithm using small data sets and the semisupervised training principle. Method-induced errors are evaluated using the Monte Carlo test and an overlapping table is proposed for the rejection or tentative acceptance of particular segmentation and fractal analysis algorithms. This study uses lung histological slides and the results show that 2D box-counting substantially outweighs lacunarity for considered configurations. These findings also suggest that the proposed method is applicable for further designing of classification algorithms, which is essential for researchers, software developers, and forensic pathologist communities. View Full-Text
Keywords: method-induced errors; fractals; lacunarity; multi-parameter box-counting; autoencoders; convolutional neural networks; image segmentation; microscopic lung images method-induced errors; fractals; lacunarity; multi-parameter box-counting; autoencoders; convolutional neural networks; image segmentation; microscopic lung images
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Oszutowska-Mazurek, D.; Mazurek, P.; Parafiniuk, M.; Stachowicz, A. Method-Induced Errors in Fractal Analysis of Lung Microscopic Images Segmented with the Use of HistAENN (Histogram-Based Autoencoder Neural Network). Appl. Sci. 2018, 8, 2356.

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