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CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images
(2023-02-01)
Kanjanasurat, Isoon
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Tenghongsakul, Kasi
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Purahong, Boonchana
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Lasakul, Attasit
The 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.
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Image Enhancement and 27 Pretrained Convolutional Neural Network Models for Diabetic Retinopathy Grading
(2023-01-01)
Kanjanasurat, Isoon
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Anuwongpinit, Thanavit
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Purahong, Boonchana
Diabetic retinopathy (DR) affects the retina's blood vessels and causes vision loss. Fundus images are used to diagnose DR, which is a lengthy process because experienced clinicians must accurately diagnose the disease and identify microlesions early to prevent blindness. Computer vision can be used for retinal image classification. The APTOS dataset contains 5990 normal, moderate, mild, proliferate, and severe retinal images. In this study, we proposed a convolutional neural network (CNN) ensemble for DR fundus grading. Each image channel was enhanced by contrast-limited adaptive histogram equalization (CLAHE) and gamma correction and then fed to 27 pretrained CNN models for one-time training to examine the DR grading. The results showed that MobileNet's green channel with the CLAHE technique is sufficiently fast and accurate for disease classification. The grading retinal images had an accuracy of 96.95%, a precision of 96.17%, a sensitivity of 97.80%, an F1 score of 96.98%, and a specificity of 97.75%. In addition, the proposed method improves the speed and robustness of retinal DR grading.
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Inventory Management System based on IoT and Microservices Architecture Design
(2023-01-01)
Sithiyopasakul, Paisan
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Piyatananugoon, Chavinkorn
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Chaowalittawin, Vasutorn
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Krungseanmuang, Woranidtha
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Sathaporn, Posathip
This 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.
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Deep transfer learning for brain tumor detection based on MRI images
(2023-01-01)
Tenghongsakul, Kasi
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Kanjanasurat, Isoon
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Archevapanich, Tuanjai
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Purahong, Boonchana
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Lasakul, Attasit
Brain 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%.
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Medical Drone Managing System for Automated External Defibrillator Delivery Service
(2022-04-01)
Purahong, Boonchana
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Anuwongpinit, Thanavit
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Juhong, Aniwat
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Kanjanasurat, Isoon
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Pintaviooj, Chuchart
One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 meters of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID‐19 pandemic.
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Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images
(2022-01-01)
Kanjanasurat, Isoon
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Domepananakorn, Nontacha
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Archevapanich, Tuanjai
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Purahong, Boonchana
This paper compares two image enhancement techniques with five convolutional neural network (CNN) models to classify Covid-19 chest x-ray images. a contrast limited adaptive histogram (CLAHE) and gamma correction which is method to improve image histogram are compared with the original chest x-ray image. We use five publicly available pre-trained CNN models to detect COVID-19: MobileNet, MobileNetV2, DenseNet169, DenseNet201, and ResNet50V2. Our procedure was validated using the COVID-19 radiography database, which is a freely accessible resource. MoblileNet with gamma correction is well-suited for COVIC-19 classification, achieving an accuracy score of 87.53 percent on the first epoch and 95.46 percent after training 100 epochs with the shortest computation time.
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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)
Purahong, Boonchana
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Anuwongpinit, Thanavit
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Kanjanasurat, Isoon
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Chansuthirangkool, Manit
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Singto, Kamphon
This article presents an educational roadmap of future trends of Metaverse in VR-based by collaborating between the School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL) with iMAKE company to make a part-time learning plan. The objectives were to measure the achievement and evaluate satisfaction with the development of part-time learning skills in technology on the topic 'Basic Metaverse based on VR'. The sample group was students in a double-degree bachelor's degree program (Dual Degree) between the School of Engineering and the Faculty of Science, KMITL: Bachelor of Engineering (IoT System and Information Engineering) and Bachelor of Science (Industrial Physics) for 16 students by selecting a specific sample group, the engineering education program has a systematic process. The results showed that the achievement of part-time learning skills development in technology on the topic 'Basic Metaverse based on VR' higher than the set criteria 74 %, the overall satisfaction is at a very good level, the mean satisfaction was 4.636 and the sample standard deviation was 0.39.
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Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service
(2022-01-01)
Sathaporn, Posathip
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Anuwongpinit, Thanavit
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Purahong, Boonchana
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.
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Egg-Counting System Using Image Processing and a Website for Monitoring
(2021-04-01)
Kanjanasurat, Isoon
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Purahong, Boonchana
This paper presents an egg-counting system method that uses a morphological operation and shows the results in real time on a website. The method begins with image acquisition from a Raspberry Pi camera and converts it to greyscale. A Gaussian blur filter is applied to reduce noise, and the blurred image is then converted to a binary image. Next, an opening operation is utilized to delete small objects from the foreground. Finally, the eggs in the image are counted using a contoured matrix, and the number of eggs is shown on a website. The system was able to count 100% of the eggs and showed the number of eggs in real time.
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Optic Disk and Fovea Localization by Using the Direction of Blood Vessels and Morphology Operation
(2021-03-17)
Kanjanasurat, Isoon
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Satayarak, Nitjaree
This paper presents the optic disk localization by using the matrix that extracted the blood vessels' direction and finding the fovea position using morphology operation in diabetic retinopathy. Our approach begins with blood vessel extraction for locating the optic disk area. Next process, the blood vessel structure was used to estimate the location of the optic disk. Next step, the morphology operator, including erosion and dilation, was used to prepare for attaining the fovea region. Finally, the location of the fovea was estimated by using the position of the optic disk, and specific characteristics of the fovea spot. The proposed method was tested on the DRIVE, DIARETDB0, and DIARETDB1 that is a public diabetic retinal image dataset. The results of the optic disk and fovea localization were compared with the ground truth image. This method can locate optic disk and fovea on DRIVE 100%. In DIARETDB0 and DIARETDB1, this algorithm can achieve optic disk 96.15% and 98.87%, respectively, and locate fovea more than 90%.

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