The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

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The use of machine learning algorithms and modern technologies for automatic segmentation of brain tissue increases in everyday clinical diagnostics. One of the most commonly used machine learning algorithms for image processing is convolutional neural networks. We present a new convolutional neural autoencoder for brain tumor segmentation based on semantic segmentation. The developed architecture is small, and it is tested on the largest online image database. The dataset consists of 3064 T1-weighted contrast-enhanced magnetic resonance images. The proposed architecture’s performance is tested using a combination of two different data division methods, and two different evaluation methods, and by training the network with the original and augmented dataset. Using one of these data division methods, the network’s generalization ability in medical diagnostics was also tested. The best results were obtained for record-wise data division, training the network with the augmented dataset. The average accuracy classification of pixels is 99.23% and 99.28% for 5-fold cross-validation and one test, respectively, and the average dice coefficient is 71.68% and 72.87%. Considering the achieved performance results, execution speed, and subject generalization ability, the developed network has great potential for being a decision support system in everyday clinical practice.

We propose a novel system for measuring finger force profiles for dexterity assessment. The system consists of a software application and encased hardware. The sensing part of the system consists of ten high sensitivity strain gage force sensors, one for each finger. The developed application controls turning ON/OFF of the LEDs (each LED corresponds to one finger) to signalize the user which finger should press the corresponding strain gage force sensor. Linearity, repeatability, and sensitivity to position pressure of the strain gage force sensors were tested to prove the system reliability. The potential usage of the system with the assessment of quantitative parameters was demonstrated on two healthy subjects.

We propose a novel system for measuring finger force profiles for dexterity assessment. The system consists of a software application and encased hardware. The sensing part of the system consists of ten high sensitivity strain gage force sensors, one for each finger. The developed application controls turning ON/OFF of the LEDs (each LED corresponds to one finger) to signalize the user which finger should press the corresponding strain gage force sensor. Linearity, repeatability, and sensitivity to position pressure of the strain gage force sensors were tested to prove the system reliability. The potential usage of the system with the assessment of quantitative parameters was demonstrated on two healthy subjects.