• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2013년도 춘계학술대회 논문집
• /
• pp.987-988
• /
• 2013

• 한국멀티미디어학회논문지
• /
• 제24권4호
• /
• pp.519-527
• /
• 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
• /
• 제21권2호
• /
• pp.198-204
• /
• 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
• /
• 제23권5호
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권1호
• /
• pp.105-125
• /
• 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.

• 대한영상의학회지
• /
• 제84권5호
• /
• pp.1080-1090
• /
• 2023

목적 이 연구의 목적은 한국 인구에서 NeuroQuant (이하 NQ)와 DeepBrain (이하 DB)에서 측정된 뇌 용적과 정상규준 백분위수를 비교하고, 두개골 형태와의 연관성을 확인하는 것이다. 대상과 방법 이 연구는 구조적 뇌 이상이 없는 163명의 한국인을 대상으로 NQ와 DB에서 측정된 뇌 용적과 정상규준 백분위수를 비교하고, 두개골 형태와의 연관성을 확인한 후향적 연구이다. 급내상관계수분석과 선형분석의 통계학적 분석을 시행하였다. 결과 정상규준 백분위수는 시상(90.8 vs. 63.3 percentile), 피각(90.0 vs. 60.0 percentile), 그리고 두정엽(80.1 vs. 74.1 percentile)에서 NQ가 DB보다 더 큰 것으로 나타났고, 후두엽(18.4 vs. 68.5 percentile)은 DB가 NQ보다 큰 것으로 나타났으며, 특히 후두엽의 경우 두개골 형태와의 비교 연구에서 유의미한 연관성을 보였고, 두개골의 전후 방향의 길이와 비례하고 상하방향의 길이와는 반비례했다. 결론 NQ와 DB에서 얻은 정상규준 백분위는 각 뇌 영역마다 유의미한 차이를 보였고, 그 차이는 한국 인구의 두개골 형태와 유의미한 관계가 있었다.

• Journal of Korean Neurosurgical Society
• /
• 제57권4호
• /
• pp.303-306
• /
• 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
• /
• 제57권2호
• /
• pp.143-146
• /
• 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
• /
• 제52권3호
• /
• pp.261-263
• /
• 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
• /
• 제45권5호
• /
• pp.303-305
• /
• 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.