Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

Questioning is widely acknowledged as an effective instructional strategy used by teachers in their interaction with students for variety of purposes. In educational practices, the analysis of classroom questions asked by teachers is of particular benefits. This paper investigates the effectiveness of machine learning techniques on analyzing teacher's classroom questions by automatically classifying them into different cognitive levels identified in Bloom's taxonomy. More specifically, this paper reports on three of the most effective machine learning techniques for text classification: k-Nearest Neighbors, Naïve Bayes, and Support Vector Machine using term frequency as a term selection approach. In doing so, a dataset of questions has been collected and classified manually into Bloom's cognitive levels. Preprocessing steps have been applied to convert questions into a representation suitable for machine learning techniques. Using this dataset, the performance of the three machine learning techniques has been evaluated. The results show a comparable performance of k-Nearest Neighbor and Naïve Bayes and a superior performance of Support Vector Machine in term of F1 and accuracy. Moreover the results also indicate that machine learning techniques show different levels of sensitivity to the number of terms used for question representation