• Genomics & Informatics
• /
• 제19권3호
• /
• pp.22.1-22.5
• /
• 2021

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

• 한국지능시스템학회논문지
• /
• 제19권3호
• /
• pp.297-303
• /
• 2009

본 논문에서는 비선형 변환에 의해 입력신호를 고차원의 확장공간으로 변환한 후, 주성분분석기법(PCA)에 의해 신호의 특징을 추출하는 기법을 제안한다. 특징추출을 위해 사용되는 기존의 주성분분석기법은 입력데이터가 비선형 특성을 갖는 경우 최적의 변환행렬을 구할 수 없다는 문제점을 가지고 있다. 이러한 문제점을 해결하기 위해, 확장공간상에서 구간별로 입력데이터를 분할한 후 주성분분석기법에 의해 구간별 특징을 추출하는 서브패턴기반 주성분분석기법(SpPCA)을 적용하였다. 다음 단계인 분류단계에서는 MLP 비선형분류기를 이용하여 구간마다 추출된 특징벡터를 이용하여 기준패턴과의 유사도를 산출한다. 최종 분류단계에서는 MLP에 의해서 산출된 유사도에 기반을 둔 융합법칙에 의하여 생체 스펙트럼 패턴을 분류한다. 제안된 방법의 유용성을 보이기 위한 실험결과에서 기존의 방법들에 비해서 향상된 인식결과를 보임을 확인하였다.

• 대한의용생체공학회:의공학회지
• /
• 제43권1호
• /
• pp.45-51
• /
• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• 대한의용생체공학회:의공학회지
• /
• 제41권2호
• /
• pp.94-100
• /
• 2020

The purpose of this study was to present the models for classifying the wrist X-ray images by types and for segmenting the radius automatically in each image using deep learning and to verify the learned models. The data were a total of 904 wrist X-rays with the distal radius fracture, consisting of 472 anteroposterior (AP) and 432 lateral images. The learning model was the ResNet50 model for AP/lateral image classification, and the U-Net model for segmentation of the radius. In the model for AP/lateral image classification, 100.0% was showed in precision, recall, and F1 score and area under curve (AUC) was 1.0. The model for segmentation of the radius showed an accuracy of 99.46%, a sensitivity of 89.68%, a specificity of 99.72%, and a Dice similarity coefficient of 90.05% in AP images and an accuracy of 99.37%, a sensitivity of 88.65%, a specificity of 99.69%, and a Dice similarity coefficient of 86.05% in lateral images. The model for AP/lateral classification and the segmentation model of the radius learned through deep learning showed favorable performances to expect clinical application.

• 대한의용생체공학회:의공학회지
• /
• 제20권4호
• /
• pp.435-442
• /
• 1999

본 연구에서는 PVC를 분류하기 위하여 웨이브렛 계수를 기반으로 하는 fuzzy-ART 네트워크를 설계하였다. 설계된 네트워크는 feature를 추출하는 부분과 fuzzy-ART 네트워크를 학습시키는 부분으로 구성된다. 우선 feature의 문턱치 구간을 설정하기 위하여 심전도 신호의 QRS를 검출하였고, 검출된 QRS는 Haar 웨이브렛을 이용한 웨이브렛 변환에 의해 주파수 분할하였다. 분할된 주파수 중에서 입력 feature를 추출하기 위하여 저주파 영역의 6번째 계수(D6)만을 선택하였다. D6신호는 입력 feature를 구성하기 위한 문턱치를 적용하여 fuzzy-ART 네트워크의 2진수 입력 feature로 전환하였고, PVC를 분류하기 위하여 fuzzy-ART네트워크를 학습시켰다. 본 연구의 성능을 평가하기 위하여 PVC가 포함된 MIT/BIH 데이터 베이스가 사용되었으며, fuzzy-ART 네트워크의 분류성능은 96.25%이었다.

• 센서학회지
• /
• 제13권6호
• /
• pp.436-445
• /
• 2004

In this study, a uroflowmetry system was developed to detect a voiding symptom conveniently at home or hospital. A implemented hardware was composed of mechanism and system circuit part, the software was developed to process uroflow data, graph display, extraction of parameter, and evaluation of congregate rate so as to analysis obtaining uroflow data. The following experiment was performed to evaluate an ability of classification and fitness. The curve pattern of uroflow was classified into each symptom. Various parameters were calculated in the curve pattern of each uroflow as follows. The parameters are MFR, AFR, VOL, VT, and FT. A significant difference among parameters was examined by a statistical analysis for extracted parameters between normal and abnormal experimental group. The uroflow data with the various symptom was divided into normal and abnormal group using fuzzy classifier. The result of the fuzzy classification using MFR and AFR was superior by 91.23 % than grouping evaluation including VOL.

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

• 한국멀티미디어학회논문지
• /
• 제25권3호
• /
• pp.451-460
• /
• 2022

Recently, several deep learning studies have been reported to automatically identify the location of diagnostic devices using endoscopic data. In previous studies, there was no design to determine whether the configuration of the dataset resulted in differences in the accuracy in which artificial intelligence models perform image classification. Studies that are based on large amounts of data are likely to have different results depending on the composition of the dataset or its proportion. In this study, we intended to determine the existence and extent of accuracy according to the composition of the dataset by compiling it into three main types using larynx, esophagus, gastroscopy, and laryngeal endoscopy images.

• 대한의용생체공학회:의공학회지
• /
• 제40권2호
• /
• pp.62-67
• /
• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• 실천공학교육논문지
• /
• 제16권1_spc호
• /
• pp.59-64
• /
• 2024

고령화가 심화되면서 암 발병률이 증가하고 있다. 피부 암은 외적으로 보이지만 사람들이 알아채지 못하거나 가볍게 간과하는 경우가 많다. 이에 초기 발견 시기를 놓쳐 말기의 경우 생존율이 7.5~11%로 사망에 이를 수 있다. 하지만 피부 암을 진단함에 있어 육안으로 진단하는 것이 아닌 정밀검사, 세포 검사 등 시간과 비용이 많이 든다는 단점이 있다. 따라서 본 연구에서는 이러한 단점을 해결하기 위해 Attention CNN 모델 기반 피부암 분류 시스템을 제안한다. 이 시스템은 전문의로 하여금 피부 암을 초기에 발견하여 신속한 조치를 취할 수 있도록 하는데 큰 도움을 줄 수 있다. 피부암 종류에 따른 이미지 데이터 불균형 문제에서 분포 비율이 낮은 데이터에는 Over Sampling 기법을, 분포 비율이 높은 데이터에는 Under Sampling 기법을 적용하여 완화하고 Attention layer가 없는 모델과 있는 모델을 비교하여 Attention layer가 없는 사전학습 모델에 추가한 피부암 분류 모델을 제안한다. 또한, 특정 클래스에 대하여 데이터 증강 기법을 강화하여 데이터 불균형 문제를 해결할 계획이다.