• Journal of Korean Elementary Science Education
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• v.27 no.1
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• pp.60-74
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• 2008

The purpose of this study was to examine the effects of a brain-based science teaching and learning model on the science related attitudes, scientific inquiry skills and science knowledge of the 2nd graders in Intelligent Life course. For this study, 117 elementary students from four classes of the 2nd grade in Seoul were selected. In the comparison group, traditional instruction was implemented and in the experimental group, instruction according to brain-based science teaching and learning model was implemented for four weeks. The results of this study were as follows : There were little differences between the comparison and experimental groups in terms of the science related attitudes except for the sub-domains of interest and curiosity. And brain-based science teaching and learning model programs improved a few scientific inquiry skills, especially observation and classification. In addition, the experimental groups showed a positive effect on science knowledge. In conclusion, brain-based science teaching and learning model programs were more effective in improvement of the science related attitudes, scientific inquiry skills and science knowledge of elementary students.

• ETRI Journal
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• v.46 no.2
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Cartoon and Animation Studies
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• s.38
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• pp.71-91
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• 2015

This study is a process to study the life drawing teaching method considering professional characteristics in animation and has a study objective to design the model and teaching method which applies the strategies considering the creative mechanism of the brain. Recently, study results about integrated teaching method are being announced which apply brain based learning principles as the alternative arguments about teaching methods in each area based on creativeness. In other words, integrated education based on creative mechanism in the brain is applied not only to fine arts and drawing education, but also to the entire areas of the arts. Life drawing is an area which demands comprehensive teaching method that vivid expressions could be skillfully obtained by understanding the communication methods with the objects through cognitive senses, creativeness and movements beyond the structural knowledge about human body. Therefore in this study, the strategies and methods for the skillfulness of life drawing and consequently arranged education model structure drawing are to be designed based on the creativeness, study materials and content factors which were analyzed in previous stages of this study. In order to combine the content factors based on creativeness and study materials of the brain which are the results of previous studies, the conclusion has been reached that 5 step cognitive strategy stages to wake brain senses, flexibilize the brain, purify the brain, integrate the brain and become the master of the brain. Strategic methods to execute this were designed with brain gym, right brain energization drawing and HSP(high-level cognizance) training. Teaching and learning model structure diagram which is designed based on this is to be continued to teaching and learning guidelines during the relevant semesters after the research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.541-550
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• 2009

This research attempted to find out any implications for strategies to design and develop the connections between the activities of the brain function and the fields of English learning (dictation, word level, speaking, word memory, listening). Thus, in developing the brain based learning model for the English education, attempts need to be made to help learners to keep the whole brain toward learning. On this point, this study indicated the significant results for the exclusive brain location and the brainwaves on the each English learning field by the quantitative EEG analysis. The results of this study presented the guidelines for the balanced development of the left brain and the right brain to train the specific site of the brain connected to the English learning fields. In addition, whole brain training model is developed by the quantitative EEG data not by the theoretical learning methods focused on the right brain training.

• Journal of Korean Elementary Science Education
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• v.24 no.1
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• pp.86-101
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• 2005

In this study, a brain-basrd model for science teaching and learning was developed based on the natural processes which human acquire knowledge about a natural object or on event, the major domains of science educational objectives of the national curriculum, and the human brain's organizational patterns and functions. In the model, each educational objective domain is related to the brain regions as follows: The affective domain is related to the limbic system, especially amygdala of human brain which is involved in emotions, the psychomotor domain is related to the occipital lobes of human brain which perform visual processing, temporal lobes which perform functions of language generating and understandng, and parietal lobes which receive and process sensory information and execute motor activities of body, and the cognitive domain is related to the frontal and prefrontal lobes which are involved in think-ing, planning, judging, and problem solving. The model is a kind of procedural model which proceed fiom affective domain to psychomotor domain, and to cognitive domain of science educational objective system, and emphasize the order of each step and authentic assessment at each step. The model has both properties of circularity and network of activities. At classrooms, the model can be used as various forms according to subjects and student characteristics. STS themes can be appropriately covered by the model.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.616-626
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• 2022

This study aims to investigate the effect of the application of the brain-based learning model on the self-efficacy, creative problem-solving ability, and academic achievement of elementary school students in science classes. The participants consisted of 22 students from one class (experimental group) and 22 students from another class (comparison group) of J Elementary School in B Metropolitan city. The experimental group conducted science classes that applied the brain-based learning model, and the comparison group conducted general explanatory science classes according to textbooks and the guide books of the teachers. The study found that science classes that applied the brain-based learning model exerted positive effects on the three abovementioned skills. Based on the results, the study confirmed that the application of the model is an effective learning tool that increases the self-efficacy, creative problem-solving ability, and academic achievement of for elementary school students in science classes.