• 대한방사선기술학회지:방사선기술과학
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• 제46권4호
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• 대한산업공학회지
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• 제27권3호
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• pp.305-314
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• 2001

In this paper, we construct an automatic evaluation system of psychological feeling for corporate identity (CI) symbol mark based on a fuzzy neural network technique. The system is modelled by trainable fuzzy inference rules with several input variables (qualitative and quantitative design components of CI symbol mark) and a single output variable (consumer's feeling). The back propagation learning algorithm, which is a conventional learning method of multilayer feedforward neural networks, is used for parameter identification of the fuzzy inference system. The learning ability to train data and the generalization ability to test data are evaluated for the proposed evaluation system by computer simulations.

• 전자공학회논문지B
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• 제31B권9호
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• pp.159-165
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• 1994

In this paper, we propose a multi-layer associative neural network structure suitable for hardware implementaion with the function of performance refinement and improved robutst capability. Unlike other methods which reduce network complexity by putting restrictions on synaptic weithts, we are imposing a requirement of hidden layer neurons for the function. The proposed network has synaptic weights obtainted by Hebbian rule between adjacent layer's memory patterns such as Kosko's BAM. This network can be extended to arbitary multi-layer network trainable with Genetic algorithm for getting hidden layer memory patterns starting with initial random binary patterns. Learning is done to minimize newly defined network error. The newly defined error is composed of the errors at input, hidden, and output layers. After learning, we have bidirectional recall process for performance improvement of the network with one-shot recall. Experimental results carried out on pattern recognition problems demonstrate its performace according to the parameter which represets relative significance of the hidden layer error over the sum of input and output layer errors, show that the proposed model has much better performance than that of Kosko's bidirectional associative memory (BAM), and show the performance increment due to the bidirectionality in recall process.

• International Journal of Computer Science & Network Security
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• 제22권5호
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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.

• 한국음향학회지
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• 제39권5호
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• pp.441-446
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• 2020

본 논문은 심층 신경망을 이용한 화자 인증(Speaker Verification, SV) 시스템에서, 심층 신경망 내부에 존재하는 각 특징 지도(Feature Map)들의 분별력을 강화하기 위해 기존 특징 지도 스케일링(Feature Map Scaling, FMS) 기법을 확장한 α-FMS 기법을 제안한다. 기존의 FMS 기법은 특징 지도로부터 스케일 벡터를 구한 뒤, 이를 특징 지도에 더하거나 곱하거나 혹은 두 방식을 차례로 적용한다. 하지만 FMS 기법은 동일한 스케일 벡터를 덧셈과 곱셈 연산에 중복으로 사용할 뿐만 아니라, 스케일 벡터 자체도 sigmoid 비선형 활성 함수를 이용하여 계산되기 때문에 덧셈을 수행할 경우 그 값의 범위가 제한된다는 한계가 존재한다. 본 연구에서는 이러한 한계점을 극복하기 위해 별도의 α라는 학습 파라미터를 특징 지도에 원소 단위로 더한 뒤, 스케일 벡터를 곱하는 방식으로 α-FMS 기법을 설계하였다. 이 때, 제안한 α-FMS 기법은 스칼라 α를 학습하여 특징 지도의 모든 필터에 동일 값을 적용하는 방식과 벡터 α를 학습하여 특징 지도의 각 필터에 서로 다른 값을 적용하는 방식을 각각 적용 후 그 성능을 비교하였다. 두 방식의 α-FMS 모두 심층 심경망 내부의 잔차 연결이 적용된 각 블록 뒤에 적용하였다. 제안한 기법들의 유효성을 검증하기 위해 RawNet2 학습세트를 이용하여 학습시킨 뒤, VoxCeleb1 평가세트를 이용하여 성능을 평가한 결과, 각각 동일 오류율 2.47 %, 2.31 %를 확인하였다.