• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.143-148
• /
• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• 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)
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권10호
• /
• pp.2788-2808
• /
• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• 한국수정란이식학회지
• /
• 제31권1호
• /
• pp.97-107
• /
• 2016

Although many diseases could be treated by the development of modern medicine, there are some incurable diseases including brain cancer, Alzheimer disease, etc. To study human brain cancer, various animal models were reported. Among these animal models, mouse models are valuable tools for understanding brain cancer characteristics. In spite of many mouse brain cancer models, it has been difficult to find a new target molecule for the treatment of brain cancer. One of the reasons is absence of large animal model which makes conducting preclinical trials. In this article, we review a recent study of molecular characteristics of human brain cancer, their genetic mutation and comparative analysis of the mouse brain cancer model. Finally, we suggest the need for development of large animal models using somatic cell nuclear transfer in translational research.

• Radiation Oncology Journal
• /
• 제20권1호
• /
• pp.1-16
• /
• 2002

Purpose : Three dimensional conformal radiotherapy planning is being used widely for the treatment of patients with brain tumor. However, it takes much time to develop an optimal treatment plan, therefore, it is difficult to apply this technique to all patients. To increase the efficiency of this technique, we need to develop standard radiotherapy plant for each site of the brain. Therefore we developed several 3 dimensional conformal radiotherapy plans (3D plans) for tumors at each site of brain, compared them with each other, and with 2 dimensional radiotherapy plans. Finally model plans for each site of the brain were decide. Materials and Methods : Imaginary tumors, with sizes commonly observed in the clinic, were designed for each site of the brain and drawn on CT images. The planning target volumes (PTVs) were as follows; temporal $tumor-5.7\times8.2\times7.6\;cm$, suprasellar $tumor-3\times4\times4.1\;cm$, thalamic $tumor-3.1\times5.9\times3.7\;cm$, frontoparietal $tumor-5.5\times7\times5.5\;cm$, and occipitoparietal $tumor-5\times5.5\times5\;cm$. Plans using paralled opposed 2 portals and/or 3 portals including fronto-vertex and 2 lateral fields were developed manually as the conventional 2D plans, and 3D noncoplanar conformal plans were developed using beam's eye view and the automatic block drawing tool. Total tumor dose was 54 Gy for a suprasellar tumor, 59.4 Gy and 72 Gy for the other tumors. All dose plans (including 2D plans) were calculated using 3D plan software. Developed plans were compared with each other using dose-volume histograms (DVH), normal tissue complication probabilities (NTCP) and variable dose statistic values (minimum, maximum and mean dose, D5, V83, V85 and V95). Finally a best radiotherapy plan for each site of brain was selected. Results : 1) Temporal tumor; NTCPs and DVHs of the normal tissue of all 3D plans were superior to 2D plans and this trend was more definite when total dose was escalated to 72 Gy (NTCPs of normal brain 2D $plans:27\%,\;8\%\rightarrow\;3D\;plans:1\%,\;1\%$). Various dose statistic values did not show any consistent trend. A 3D plan using 3 noncoplanar portals was selected as a model radiotherapy plan. 2) Suprasellar tumor; NTCPs of all 3D plans and 2D plans did not show significant difference because the total dose of this tumor was only 54 Gy. DVHs of normal brain and brainstem were significantly different for different plans. D5, V85, V95 and mean values showed some consistent trend that was compatible with DVH. All 3D plans were superior to 2D plans even when 3 portals (fronto-vertex and 2 lateral fields) were used for 2D plans. A 3D plan using 7 portals was worse than plans using fewer portals. A 3D plan using 5 noncoplanar portals was selected as a model plan. 3) Thalamic tumor; NTCPs of all 3D plans were lower than the 2D plans when the total dose was elevated to 72 Gy. DVHs of normal tissues showed similar results. V83, V85, V95 showed some consistent differences between plans but not between 3D plans. 3D plans using 5 noncoplanar portals were selected as a model plan. 4) Parietal (fronto- and occipito-) tumors; all NTCPs of the normal brain in 3D plans were lower than in 2D plans. DVH also showed the same results. V83, V85, V95 showed consistent trends with NTCP and DVH. 3D plans using 5 portals for frontoparietal tumor and 6 portals for occipitoparietal tumor were selected as model plans. Conclusion : NTCP and DVH showed reasonable differences between plans and were through to be useful for comparing plans. All 3D plans were superior to 2D plans. Best 3D plans were selected for tumors in each site of brain using NTCP, DVH and finally by the planner's decision.

• 한국동물번식학회:학술대회논문집
• /
• 한국동물번식학회 2001년도 춘계학술발표대회
• /
• pp.47-47
• /
• 2001

The neuropeptide vasopressin (VP) is a nine- amino acid hormone synthesized as preprohormone in the cell bodies of hypothalamic magnocellular neurons. The tumor in magnocellular neurons of the hypothalamus is associated with disfunctions of the cell bodies, leading to the diabetes insipidus. In order to study with the diabetes insipidus caused by a defect in VP synthesis and its secretion, we have produced the transgenic mice regulated by vasopressin promoter inserted to SV40 T antigen coding sequence (pVPSV.IGR2.1). One transgenic line expressing high levels of SV40 T antigen was propagated. The founder and all transgene positive adult animals have appeared with shorten mortality or apparent phenotypic abnormalities, including immune complex disease, and eventually die between 4 and 8 months of age. The mRNA and protein of SV40T antigen transgene were detected in brain of fetus as well as in brain, spleen, lung and lymph node in moribund at the age of 20 weeks. Histological analysis of transgenic mice showed that tumor developed in brain similar to primitive neuroectodermal tumors (PNET) in man. We also detected lymphomas in spleen and lymph node, and consequent tumor formation in various tissues of the transgenic mice. In pVPSV.IGR2.1, 21% mice showed brain tumor (PNET) at 5 weeks and 100% mice showed brain tumor after 15 weeks. In addition, Expression of apoptosis related genes (Bcl-28 & Bax) was increased over their age in mice with PNET as compared to control mice. Apoptosis related gene expression might be deregulated in mice with brain tumor. However, transgenic mice were not developed with the diabetes insipidus. These mice represent the first disease model to exhibit primitive neuroectodermal tumor in brain, as well as a unique model system for exploring the cellular pathogenesis of lymphomas.

• 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.

• Journal of Korean Neurosurgical Society
• /
• 제63권1호
• /
• pp.26-33
• /
• 2020

Glioblastoma (GBM) is a disease without any definite cure. Numerous approaches have been tested in efforts to conquer this brain disease, but patients invariably experience recurrence or develop resistance to treatment. New surgical tools, carefully chosen samples, and experimental methods are enabling discoveries at single-cell resolution. The present article reviews the cell-of-origin of isocitrate dehydrogenase (IDH)-wildtype GBM, beginning with the historical background for focusing on cellular origin and introducing the cancer genesis patterned on firework. The authors also review mutations associated with the senescence process in cells of the subventricular zone (SVZ), and biological validation of somatic mutations in a mouse SVZ model. Understanding GBM would facilitate research on the origin of other cancers and may catalyze the development of new management approaches or treatments against IDH-wildtype GBM.

• International Journal of Control, Automation, and Systems
• /
• 제2권1호
• /
• pp.100-106
• /
• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.