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CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning
(2022-01-01)
Apivanichkul, Miss Kamonchat
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Phasukkit, Pattarapong
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Pittaya, Dankulchai
This paper proposed to insert additional feature into input datasets (i.e., CT scans) for automatic femur segmentation model, U-Net, with respect to increase the accuracy of model performance. An additional feature is available reference information representing identity on each CT scans and has an effect on results of deep learning model training. In this experiment, choose the left-femur as the target organ, which is common organs-At-risk (OARs) for lower abdominal cancers. The automatic femur segmentation model training was separately executed through two different datasets, one cropped-dataset with additional feature and one original dimension dataset without additional feature. For additional feature, lying posture of patient when entered the CT scanner was selected. The performance results of both trained U-Net models were compered in order to observe the difference of effect. Evaluation results reported that the additional feature could increase an accuracy and precision including support prediction for the left-femur segmentation, with the Dice Similarity Coefficient (DSC) of 61.573% and Intersection Over Union (IoU) of 45.621%, respectively. Specifically, deep learning combining additional feature insertion on cropped-datasets was the novelty in this experiment to effectively segment the left femur.
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Deep Learning-Based Acoustic Emission Scheme for Rail Crack Monitoring
(2021-01-01)
Suwansin, Wara
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Phasukkit, Pattarapong
This research proposes a single-sensor acoustic emission (AE) scheme for detection and localization of crack in steel rail (rail head, rail web, and rail foot) under load. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were used total variation denoising (TVD) algorithm to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train (80 % of the input data) and test (20%) the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented on-site to detect cracks in the steel rail. The total accuracy under the first and second groupings were 86.6 % and 96.6 %. The novelty of this research lies in the use of single AE sensor and AE signal-driven deep learning algorithm to detect and localize cracks in the steel rail, unlike conventional AE crack-localization technology which relies on two or more sensors and human interpretation.
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Hand Movement Classification Base on EEG Signals using Deep Learning and Dimensional Reduction Technique
(2019-11-01)
Boonme, Phattraporn
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Thongserm, Petchanon
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Arunsuriyasak, Peerachai
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Phasukkit, Pattarapong
This research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm. It's well-known that Electroencephalography (EEG) shows neural oscillation behaviors in electrical voltage form. Also, Brain-Computer Interface (BCI) is direct communication between neural oscillation and computer to control machines without physical movements. So, this paper aims to present the classification method of EEG signals data to develop a BCI in the future. By using deep learning to classification data is classified into raise the right arm, raise the left arm. And decrease EEG signal data by using Principal Component Analysis (PCA). PCA can reduce the data size of EEG signal from 1000x28 to 28x28. Experimental result of classification has accuracy 90.86% and 94.71%
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Doppler radar for dynamic hand gesture recognition based on signal image processing
(2019-07-01)
Arthamanolap, Kongphum
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Gabbualoy, Somprasong
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Phasukkit, Pattarapong
From previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.

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