Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images.

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Angiogenic inhibition therapy, a sliding mode control adventure.

BACKGROUND AND OBJECTIVE: A sliding mode based inhibitory agent injection law is derived using angiogenic inhibition model of cancer progression which describes the variation of tumor and supporting vasculature volumes in targeted molecular therapies. METHODS: The closed loop injection laws are derived by applying sliding mode control method which is known as a robust control approach. It is beneficial especially when there are parametric uncertainties in the dynamical model of the plant. In this research plant is represented by angiogenic cancer progression model. Random uncertainties are introduced to the physiological rate constants and simulations are repeated several times to see the deviations in the states and inhibitory agent rates. RESULTS: Smooth inhibitory agent injection laws are obtained from the developed approach. Several different control configurations reveal that, it is possible to decrease the setup time to 6.1 days. A few of those settings failed to generate a satisfactory result. It appeared also that the sliding surface parameters have a distinct effect on the closed loop performance. Appropriate choice of the sliding surface parameters allows one to have a robust closed loop treatment where the deviation from the nominal response is relatively lower. DISCUSSION: The lowest setup time obtained in this research is 6.1 days. This appear shorter than other similar studies where the plant is represented by the same or similar models. In the cases where the setup time is relatively shorter, the inhibitory agent injection requirement is higher than the other cases. This result seems larger compared to similar studies however the inhibitory agent stays at high levels for a short duration. In addition, the existence of uncertainty may also lead to an increase in the inhibitory agent rate requirements. Nevertheless, the results of the study reveals that one can reduce the tumor volume in a finite time without the necessity of constant application of high dosage inhibitory agent.

Estimating the Parameters of Fitzhugh-Nagumo Neurons from Neural Spiking Data.

A theoretical and computational study on the estimation of the parameters of a single Fitzhugh-Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge.