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

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.169-174
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• 2013

In counterfire warfare, it is important to detect and attack enemy targets faster than the enemy using sensing The grided environmental data is usually provided by the numerical simulation coupled with a data assimilation technique and various inter- or extrapolation algorithms, both of which are based on the observation spanning from simple equipments to satellites. In order to employ the gridded environmental data in the M&S system frequently cutting area and changing its resolution, interpolation algorithms such as linear, cubic spline, IDW, and Kriging methods are necessary to apply. These methods, however, require much time in the M&S system. This paper introduces a technic to reduce time to change the resolution of data. using the binary search method, which finds a point to interpolate quickly and interpolate data in the vicinity of. We also show the efficiency of proposed methods by way of measuring the respective elapsed times.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.143-148
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• 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.