• Korean Journal of Radiology
• /
• v.23 no.10
• /
• pp.949-958
• /
• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.24 no.1
• /
• pp.12-16
• /
• 1988

The observation of the tidal current and the bottom contour around the set net fishing ground were carried out at four different regions of the southern part of Korea in order to obtain the basic information on the migrating course of fishes. The bottom contour was surveyed with portable echo sounder, and the tidal current was observed by two different methods at the same time. One was 25 hour observation at the fixed position with self-recording current meter (Inter Ocean Model 135 type) and the other was the drift observation of radar reflectors. Most of the set nets have been set near bottom valleys. It was regarded that the fish school became to dense easily near the valley according to the combined effect of the tidal current and the bottom contour.

• Journal of Biomedical Engineering Research
• /
• v.42 no.4
• /
• pp.167-174
• /
• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• Advances in nano research
• /
• v.12 no.2
• /
• pp.185-195
• /
• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of Biomedical Engineering Research
• /
• v.44 no.3
• /
• pp.182-189
• /
• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.329-335
• /
• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.399-401
• /
• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.

• Journal of Powder Materials
• /
• v.16 no.6
• /
• pp.403-409
• /
• 2009

Plasma spray forming is recently explored as a near-net-shape fabrication route for ultra-high temperature metals and ceramics. In this study, monolithic tungsten has been produced using an atmospheric plasma spray forming and subsequent high temperature sintering. The spray-formed tungsten preform from different processing parameters has been evaluated in terms of metallurgical aspects, such as density, oxygen content and hardness. A well-defined lamellae structure was formed in the as-sprayed deposit by spreading of completely molten droplets, with incorporating small amounts of unmelted/partially-melted particles. Plasma sprayed tungsten deposit had 84-87% theoretical density and 0.2-0.3 wt.% oxygen content. Subsequent sintering at 2500$^{\circ}C$ promoted the formation of equiaxed grain structure and the production of dense preform up to 98% theoretical density.

• Journal of the Korean Ceramic Society
• /
• v.35 no.4
• /
• pp.406-412
• /
• 1998

The reaction bonded alumina ceramics was prepared through the addition of each SiC and ZrO2 powder to the mixture of Al metal powder and Al2O3 The mono sized (3mm) and biodal sized (3mm+5mm) balls were used in attrition milling of Al and starting powders. The milling efficiency of both cases was compared by the analysis of particle size and X-ray diffraction. After the forming and sintering of each powder batchs the weight gains dimensional changes and densities were determined. The specimens were investigated by X-ray diffraction analysis and scanning electron microscope. Bimodal sized balls had better milling effect than single ball size in the milling of Al powder. However in the milling which ceramic powders mono sized the green body during the reaction sintering at 1$600^{\circ}C$ for 5 hour was about 10% The densities attained the values of 92-98% theoretical. The SiC added specimen that was milled with 3mm ball media had 96% theoretical density and dense microstructure.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1024-1026
• /
• 2003

The purpose of establishing GPS networks of continuously operating reference stations (CORS) is aimed to assist land surveying or crustal deformation in the early stage. However, with a fast evolving and improving path the GPS technique has been extended to accurately measure atmospheric precip itable water vapor as a core objective of many projects developed in many countries and regions such as the SuomiNet (U.S., UNAVCO), COST716 (European, COST), GEONET (Japan, GSI), ...etc. In this paper, we present the current progress of the being-set-up GPS network in Taiwan whose atmospheric profile observations mainly count on the traditional radiosonde soundings as typically seen in any other part of the world. The GPS data collected from the Taiwan dense GPS network primarily supported by Central Weather Bureau are processed using the Bernese software version 4.2. Precipitable water vapor is then derived with the auxiliary surface meteorological measurements. Time series of precipitable water are examined and analyzed. A focus on the extreme weather cases is shown as an example.