• Korean Journal of Radiology
• /
• v.22 no.7
• /
• pp.1194-1202
• /
• 2021

Objective: To investigate the age-dependent changes in regional cerebral blood flow (CBF) in healthy adults by fitting mathematical models to imaging data. Materials and Methods: In this prospective study, 90 healthy adults underwent pseudo-continuous arterial spin labeling imaging of the brain. Regional CBF values were extracted from the arterial spin labeling images of each subject. Multivariable regression with the Akaike information criterion, link test, and F test (Ramsey's regression equation specification error test) was performed for 7 models in every brain region to determine the best mathematical model for fitting the relationship between CBF and age. Results: Of all 87 brain regions, 68 brain regions were best fitted by cubic models, 9 brain regions were best fitted by quadratic models, and 10 brain regions were best fitted by linear models. In most brain regions (global gray matter and the other 65 brain regions), CBF decreased nonlinearly with aging, and the rate of CBF reduction decreased with aging, gradually approaching 0 after approximately 60. CBF in some regions of the frontal, parietal, and occipital lobes increased nonlinearly with aging before age 30, approximately, and decreased nonlinearly with aging for the rest of life. Conclusion: In adults, the age-related perfusion patterns in most brain regions were best fitted by the cubic models, and age-dependent CBF changes were nonlinear.

• Mass Spectrometry Letters
• /
• v.10 no.1
• /
• pp.11-17
• /
• 2019

Drosophila melanogaster (fruits fly) is a representative model system widely used in biological studies because its brain function and basic cellular processes are similar to human beings. The whole head of the fly is often used to obtain the key function in brain-related diseases like degenerative brain diseases; however the biomolecular distribution of the head may be slightly different from that of a brain. Herein, lipid profiles of the head and dissected brain samples of Drosophila were studied using electrospray ionization-mass spectrometry (ESI-MS). According to the sample types, the detection of phospholipid ions was suppressed by triacylglycerol (TAG), or the specific phospholipid signals that are absent in the mass spectrum were measured. The lipid distribution was found to be different in the wild-type and the microRNA-14 deficiency model ($miR-14{\Delta}^1$) with abnormal lipid metabolism. A few phospholipids were also profiled by comparison of the head and the brain in two fly model systems. The mass spectra showed that the phospholipid distributions in the $miR-14{\Delta}^1$ model and the wild-type were different, and principal component analysis revealed a correlation between some phospholipids (phosphatidylethanolamine (PE), phosphatidylinositol (PI), and phosphatidylserine (PS)) in $miR-14{\Delta}^1$. The overall results suggested that brain-related lipids should be profiled using fly samples after dissection for more accurate analysis.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.73-88
• /
• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.4 no.4
• /
• pp.97-106
• /
• 1996

The impact response of the human brain has been determined by three-dimensional finite element modeling. The model includes a layered shell closely representing the cranial bones with the interior contents occupied by an incompressible contimuum to simulate the brain. Flax and tentorium modeled with 4 node membrane element were also incorporated. The computed pressure-time histories at 4 locations within the brain element compared quite favorably with previously published experimental data from cadaver experiments. A parametric study was subsequently conducted to identify the model response when the impact were varied.

• Journal of Korean Elementary Science Education
• /
• v.27 no.1
• /
• pp.60-74
• /
• 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
• /
• v.46 no.2
• /
• pp.263-276
• /
• 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.

• International Journal of Knowledge Content Development & Technology
• /
• v.9 no.1
• /
• pp.57-79
• /
• 2019

This study attempts to seek a solution, in terms of brain science, to stimulate reading activities of men to whom no attention has been given from the field of reading. In order to do so, brain pattern, reading tendency, reading preference, reading effectiveness and influence of reading were analyzed. As a result of the analysis, first, it showed that respondents' characteristics did not affect brain patterns, but school and social influences on reading were found to affect brain patterns. Second, reading propensity according to gender was observed to be different in terms of personal health, personal self - esteem, and cultural artistry. On the other hand, reading effectiveness was found to be different in terms of reading engagement and the willingness to continue reading whereas reading propensity according to the brain pattern was different in books related to humor and family matters. Third, reading satisfaction, reading engagement and willingness to continue reading all were observed to affect the reading activities of men. Suggesting measures to stimulate reading activities of men based on such findings, first, implementing dynamic reading education programs and finding reading models for men are needed. Second, when selecting books for reading program operations, books should be recommended according to gender rather than being selected en bloc by libraries. Third, since reading education at home shows high influences on both male and male-type brain pattern, the starting point of reading education should be made at homes. In particular, fathers, who can become a role model for men, need a reading role model, and reading education programs for fathers are also required.

• Knowledge Management Research
• /
• v.6 no.2
• /
• pp.1-22
• /
• 2005

Knowledge is recognized as the most important asset among enterprises. Therefore, the necessity of knowledge management is ever on the increase nowadays. While many people have endeavored to develop knowledge storage, sharing and usage, knowledge creation is not sufficiently investigated for practical application, because knowledge creation is largely related to creativity and difficult to establish a systematic methodology. In order to overcome such problems, the creative knowledge creation model is proposed by using the whole brain theory and creative thinking tools. First of all, the creative knowledge creation model is based on the Nonaka's knowledge creation model integrated with the whole brain theory. The whole brain theory is then used as a standard to organize a whole brain team that is composed of members who have diverse thinking patterns. For creative thinking tools, the mandal-art and the contradiction matrix of TRIZ are used for a knowledge conversion. Each process of the creative knowledge creation model is sequentially suggested and several terms are defined. In order to verify the effectiveness of the creative knowledge creation model, the proposed model is applied to the development of a dishwasher with a new concept. According to the order of the proposed method, the model is applied twice in the cycle of spiral evolution. Three kinds of dish-washing methods have been developed using the proposed model. The results of the application are then analyzed and presented.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.101-110
• /
• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

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