درخت حوزه‌های تخصصی

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

The growing interest in learning English language as an international language through international textbooks has necessitated the need for the evaluation of the cultural content embedded in English coursebooks. With regard to the close interrelation of language and culture, this study investigated the extent to which cultural aspects are represented in two international EFL textbooks. To this end, content analysis was used to analyze two commercial coursebooks (i.e., Summit 1a and Viewpoint 1), which are used in Iranian language institutes. The results of the directed content analysis revealed that two subcategories emerged from the content analysis (i.e., technology and environment), which were coded as an off-list category. Therefore, it was found that the reevaluation and extension of Stern’s (1993) framework for culture is necessary. Moreover, the results of the quantitative content analysis showed that there was not a balanced representation of cultural aspects in the selected coursebooks and the most frequent element was individual values in life. In addition, it was found that there was significant variation between the two coursebooks in the representation of culture. The study concluded that more in-depth investigation of cultural content is needed to develop cultural awareness of language learners. This study has implications for language material developers, language teachers, and language learners.