Purahong, Boonchana
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Purahong, Boonchana
Alternative Name
Purahong, B.
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Email
boonchana.pu@kmitl.ac.th
12 results
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Item type:Publication, Blood Vessel Extraction and Optic Disk Localization for Diabetic Retinopathy(2020-09-15); ; ; Benjangkaprasert, ChawalitThis paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Inventory Management System based on IoT and Microservices Architecture Design(2023-01-01); Sathaporn, PosathipThis paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Retinal Blood Vessel Extraction by Using Pre-processing and IterNet Model(2020-12-03); Lasakul, AttasitAt present, many of visual disease happened from the abnormality of retinal vessels. The automatic vascular extraction from fundus images is essential for the diagnosis to reduce vision loss. This paper offers retinal blood vessel segmentation using the pre-processing and IterNet model, a convolution neural network. The green channel and gray scale image that is high contrast between the blood vessel and background, including the normalization, were used to improve blood vessel image quality. The proposed method was tested with two widely used databases, including DRIVE and CHASEDB-1, which unique characteristics in each data set. The results of blood vessel extraction of Drive and CHASEDB-1 achieved sensitivity 0.8126 and 0.7541, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Medical Drone Managing System for Automated External Defibrillator Delivery Service(2022-04-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Personal identification using a delaunay triangle and optic disc retinal vascular pattern(2020-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Engineering Education Roadmap of the Future Trend of Basic Metaverse based on VR with cooperation between the government and the private sector(2022-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images(2023-02-01); Lasakul, AttasitThe 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep transfer learning for brain tumor detection based on MRI images(2023-01-01); Lasakul, AttasitBrain tumors are among the main causes of cancer-related mortality in humans. Early detection of brain tumors is a vital job in the medical task of diagnosis and cure planning for patients. The automatic detection greatly facilitates medical personnel. Magnetic resonance imaging (MRI) is an accepted imaging strategy for diagnosing brain tumors. Presently, deep learning approaches have proven effective in handling various computer vision problems, such as image classification, because of their high performance and also determine models that can learn and decide based on sample data. In this study, the deep transfer learning method, namely InceptionResNet-V2, ResNet50, MobileNet-V2, and VGG16, was used to compare and find the most suitable model for brain tumor detection from the public MRI dataset. Also, CLAHE was employed as an image enhancement technique to improve the quality of the image data set before being used as the model input. As a result, the suggested method performed a prediction accuracy of up to 100%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service(2022-01-01); ; This article describes a method for integrating a mobile application for controlling and transmitting voice data levels in various departments within an organization with Amazon Elastic Compute Cloud (AWS EC2) to reduce hardware location costs and create more convenient in-house management at a single point. To begin, the paper introduces the project objective with a business scenario from an organization. Second, the SIP server implementation method is provided by Asterisk on AWS EC2 Ubuntu operating system and connection with a Mobile application that is used by flutter framework. Finally, the project experiments and discussions will be presented, and the obtained results show that the call setup time for the iOS/Android platforms to PC performed the best, taking less than one second, and was the fastest when compared to other testing metrics. However, there are many more metrics that should be considered, which are presented in the research's results section. With high performance and stability, this article was able to broadcast voice data via mobile applications.