• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.987-988
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• 2013

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

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

• 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 Society of Radiology
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• v.84 no.5
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• pp.1080-1090
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• 2023

Purpose This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuant^Ⓡ (NQ) and DeepBrain^Ⓡ (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape. Materials and Methods In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured threedimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations. Results The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter. Conclusion The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.

• Journal of Korean Neurosurgical Society
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• v.57 no.4
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• pp.303-306
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• 2015

Deep brain stimulation (DBS) of the pedunculopontine nucleus (PPN) is a novel therapy developed to treat Parkinson's disease. We report a patient who underwent bilateral DBS of the PPN and subthalamic nucleus (STN). He suffered from freezing of gait (FOG), bradykinesia, rigidity and mild tremors. The patient underwent bilateral DBS of the PPN and STN. We compared the benefits of PPN-DBS and STN-DBS using motor and gait subscores. The PPN-DBS provided modest improvements in the gait disorder and freezing episodes, while the STN-DBS failed to improve the dominant problems. This special case suggests that PPN-DBS may have a unique role in ameliorating the locomotor symptoms and has the potential to provide improvement in FOG.

• Journal of Korean Neurosurgical Society
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• v.57 no.2
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• pp.143-146
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• 2015

Chorea-acanthocytosis (ChAc) is a rare hereditary disorder characterized by involuntary choreiform movements and erythrocytic acanthocytosis. Pharmacotherapy for control of involuntary movements has generally been of limited benefit. Deep brain stimulation (DBS) has recently been used for treatment of some refractory cases of ChAc. We report here on the effect of bilateral high-frequency DBS of globus pallidus interna in a patient with ChAc.

• Journal of Korean Neurosurgical Society
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• v.52 no.3
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• pp.261-263
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• 2012

The experience of pediatric deep brain stimulation (DBS) of the globus pallidus internus (GPi) in the treatment of early-onset DYT1 generalized dystonia is still limited. Here, we report the surgical experience of bilateral GPi-DBS under general anesthesia by using microelectrode recording in a 7-year-old girl with early-onset DYT1 generalized dystonia. Excellent improvement of her dystonia without neurological complications was achieved. This case report demonstrates that GPi-DBS is an effective and safe method for the treatment of medically refractory early-onset DYT1 generalized dystonia in children.

• Journal of Korean Neurosurgical Society
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• v.45 no.5
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• pp.303-305
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• 2009

The stereotactic surgical target for dystonic tremor is the subject of ongoing debate. Targeting the subthalamic area using deep brain stimulation has been regaining interest as a therapy for various types of involuntary movements. We describe the efficacy of stimulation of the subthalamic area in a patient with intractable dystonic tremor. Excellent control without neurological complications was achieved. This case report demonstrates that the subthalamic area is a valuable target for the control of dystonic tremor.