DSpace-CRIS

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
(2024-01-01)
Purahong, Boonchana
;
Krungseanmuang, Woranidtha
;
Tenghongsakul, Kasi
;
Archevapanich, Tuanjai
;
Khunthawiwone, Parkpoom
This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.
Some of the metrics are blocked by your
consent settings
CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images
(2023-02-01)
Kanjanasurat, Isoon
;
Tenghongsakul, Kasi
;
Purahong, Boonchana
;
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.
Some of the metrics are blocked by your
consent settings
Deep transfer learning for brain tumor detection based on MRI images
(2023-01-01)
Tenghongsakul, Kasi
;
Kanjanasurat, Isoon
;
Archevapanich, Tuanjai
;
Purahong, Boonchana
;
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%.
Some of the metrics are blocked by your
consent settings
Retinal Blood Vessel Extraction by Using Pre-processing and IterNet Model
(2020-12-03)
Tenghongsakul, Kasi
;
Kanjanasurat, Isoon
;
Purahong, Boonchana
;
Lasakul, Attasit
At 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.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results