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

• 대한방사선기술학회지:방사선기술과학
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• 제46권6호
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• pp.535-542
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• 2023

This study aimed to implement a deep learning model that can perform quantitative quality control through ACTS software used for quantitative evaluation of ACR phantom in CT quality control and evaluate its usefulness. By changing the scanning conditions, images of three modules of the ACR phantom's slice thickness (ST), low contrast resolution (LC), and high contrast resolution (HC) were obtained and classified as ACTS software. The deep learning model used ResNet18, implementing three models in which ST, HC, and LC were learned with epoch 50 and an integrated model in which three modules were learned with Epoch 10, 30, and 50 at once. The performance of each model was evaluated through Accuracy and Loss. When comparing and evaluating the accuracy and loss function values of the deep learning models by ST, LC, and HC modules, the Accuracy and Loss of the HC model were the best with 100% and 0.0081, and in the integrated model according to the Epoch value, Accuracy and Loss with epoch 50 were the best with 96.29% and 0.1856. This paper showed that quantitative quality control is possible through a deep learning model, and it can be used as a basis and evidence for applying deep learning to the CT quality control.

• 태양에너지
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• 제20권3호
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• pp.39-50
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• 2000

Radiant barrier systems(RES) constructed with low emissivity materials bounded by an open air space can be used to reduce the net radiation transfer between two surfaces. To analyze the heat transfer characteristics of the radiant barrier systems which consist of a single-glass and radiation barriers, a simple theoretical model based on energy balances was suggested. And the model was validated by means of the experimental results. Using a guarded hot box, the temperatures of layers in selected RES and energy use for each cases were measured. The results show that the model well explained the heat transfer characteristics of those RES. Also, the heat transfer coefficient correlations considering natural and forced convection heat transfer ware suggested. It is found that the heat transfer efficiency of a RBS with aluminium surface improved up to 66.6% over that of a single glazing system.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.930-932
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• 2021

영상처리에서 데이터 증강(Data augmentation)은 단순히 사진을 편집하여 사진의 개수를 증강하는 것이다. 단순 데이터 증강은 동물의 반점이나 다양한 색깔을 반영하지 못하는 한계가 있다. 본 논문에서는 GAN을 통한 데이터 증강 기법을 제안한다. 제안하는 방법은 CycleGAN을 사용하여 GAN 이미지를 생성한 뒤, 데이터 증강을 거쳐 동물의 종 분류 정확도를 측정한다. 정확도 비교를 위해 일반 사진으로만 구성한 집단과 GAN 사진을 추가한 두 집단으로 나누었다. ResNet50을 사용하여 종 분류 정확도를 측정한다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.136-141
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• 2022

A very complex task in deep learning such as image classification must be solved with the help of neural networks and activation functions. The backpropagation algorithm advances backward from the output layer towards the input layer, the gradients often get smaller and smaller and approach zero which eventually leaves the weights of the initial or lower layers nearly unchanged, as a result, the gradient descent never converges to the optimum. We propose a two-factor non-saturating activation functions known as Bea-Mish for machine learning applications in deep neural networks. Our method uses two factors, beta (𝛽) and alpha (𝛼), to normalize the area below the boundary in the Mish activation function and we regard these elements as Bea. Bea-Mish provide a clear understanding of the behaviors and conditions governing this regularization term can lead to a more principled approach for constructing better performing activation functions. We evaluate Bea-Mish results against Mish and Swish activation functions in various models and data sets. Empirical results show that our approach (Bea-Mish) outperforms native Mish using SqueezeNet backbone with an average precision (AP50val) of 2.51% in CIFAR-10 and top-1accuracy in ResNet-50 on ImageNet-1k. shows an improvement of 1.20%.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.451-452
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• 2023

최근 CNN 이 다양한 산업에 확산되고 있으며, IoT 기기 및 엣지 컴퓨팅에 적합한 경량 모델에 대한 연구가 급증하고 있다. 본 논문에서는 CNN 모델의 파라미터 비트 연산을 위한 자동화 프레임워크를 제안하고, 파라미터 비트와 모델 정확도 사이의 관계를 실험 및 연구한다. 제안된 프레임워크는 하위 n- bit 를 0 으로 설정하여 정보 손실 발생시킴으로써 ImageNet 데이터셋으로 사전 학습된 CNN 모델의 파라미터와 정확도의 강인성을 비트 단위로 체계적으로 실험할 수 있다. 우리는 비트 연산을 수행한 파라미터로 InceptionV3, InceptionResnetV2, ResNet50, Xception, DenseNet121, MobileNetV1, MobileNetV2 모델의 정확도를 평가한다. 실험 결과는 성능이 낮은 모델일수록 파라미터와 정확도 간의 강인성이 높아 성능이 좋은 모델보다 정확도를 유지하는 비트 수가 적다는 것을 보여준다.

• 대한의용생체공학회:의공학회지
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• 제41권2호
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• pp.101-106
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• 2020

Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.

• 한국안전학회지
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• 제34권6호
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• pp.50-57
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• 2019

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

• 한국인터넷방송통신학회논문지
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• 제21권6호
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• pp.77-82
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• 2021

요검사는 물리적 성상 검사, 화학적 검사, 현미경 검사 세 가지가 있다. 이 중에서 화학적 요검사는 일반인이 쉽게 접근하는 방법으로 요검사지의 화학반응을 눈으로 표준비색표와 비교하거나 휴대용 요검사기를 별도로 구매하여 검사를 진행한다. 현재는 스마트폰의 보급이 대중화되어 스마트폰을 활용한 요검사 서비스 연구가 높아지고 있다. 요검사 스크리닝 애플리케이션은 스마트폰을 활용한 요검사 서비스 중 하나이다. 그러나 요검사 스크리닝 애플리케이션으로 촬영한 요검사 패드 RGB 값은 조명영향으로 인해 큰 편차가 발생한다. 요검사 패드 RGB 값의 편차는 요검사 판별의 정확도를 떨어뜨린다. 따라서 본 논문에서는 스마트폰 기반 요검사 스크리닝 애플리케이션으로 촬영한 요검사지를 검사 항목별 요검사 패드로 분류한 후 CNN을 통해 요검사 패드 이미지 판별의 정확도를 높인다. 요검사지는 다양한 배경에서 촬영하여 CNN 이미지를 생성하였으며 ResNet-50 CNN 모델을 사용하여 요검사 판별을 분석하였다.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.81-89
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• 2021

자연 조건에서 Apple 감지에는 가림 문제와 작은 대상 감지 어려움이 있다. 본 논문은 SSD 기반의 개선 된 모델을 제안한다. SSD 백본 네트워크 VGG16은 ResNet50 네트워크 모델로 대체되고 수용 필드 구조 RFB 구조가 도입되었다. RFB 모델은 작은 표적의 특징 정보를 증폭하고 작은 표적의 탐지 정확도를 향상시킨다. 유지해야 하는 정보를 필터링하기 위해 주의 메커니즘 (SE)과 결합하면 감지 대상의 의미 정보가 향상된다. 향상된 SSD 알고리즘은 VOC2007 데이터 세트에 대해 학습된다. SSD에 비해 개선 된 알고리즘은 폐색 및 작은 표적 탐지의 정확도를 3.4 % 및 3.9 % 향상 시켰다. 이 알고리즘은 오 탐지율과 누락된 감지율을 향상 시켰다. 본 논문에서 제안한 개선 된 알고리즘은 더 높은 효율성을 갖는다.