Structural MRI-Based Schizophrenia Classification Using Autoencoders and 3D Convolutional Neural Networks in Combination with Various Pre-Processing Techniques

Schizophrenia is a severe neuropsychiatric disease whose diagnosis, unfortunately, lacks an objective diagnostic tool supporting a thorough psychiatric examination of the patient. We took advantage of today’s computational abilities, structural magnetic resonance imaging, and modern machine learning methods, such as stacked autoencoders (SAE) and 3D convolutional neural networks (3D CNN), to teach them to classify 52 patients with schizophrenia and 52 healthy controls. The main aim of this study was to explore whether complex feature extraction methods can help improve the accuracy of deep learning-based classifiers compared to minimally preprocessed data. Our experiments employed three commonly used preprocessing steps to extract three different feature types. They included voxel-based morphometry, deformation-based morphometry, and simple spatial normalization of brain tissue. In addition to classifier models, features and their combination, other model parameters such as network depth, number of neurons, number of convolutional filters, and input data size were also investigated. Autoencoders were trained on feature pools of 1000 and 5000 voxels selected by Mann-Whitney tests, and 3D CNNs were trained on whole images. The most successful model architecture (autoencoders) achieved the highest average accuracy of 69.62% (sensitivity 68.85%, specificity 70.38%). The results of all experiments were statistically compared (the Mann-Whitney test). In conclusion, SAE outperformed 3D CNN, while preprocessing using VBM helped SAE improve the results

Tools for development of interactive web-based maps: application in healthcare

Interactive visualisations on the Internet have become commonplace in recent years. Based on such publicly available visualisations, users can obtain information from various domains quickly and easily. A location specific method of data presentation can be much more effective using map visualisation than using traditional methods of data visualisation, such as tables or graphs. This paper presents one of the possible ways of creating map visualisations in a modern web environment. In particular, we introduce the technologies used in our case together with their detailed configuration. This description can then serve as a guide for the customisation of the server environment and application settings so that it is easy to create the described type of visualisation outputs. Together with this manual, specific cases are presented on the example of an application which was developed to display the location of medical equipment in the Czech Republic based on data collected from healthcare providers