Poomrittigul, Suvit

Preferred name

Poomrittigul, Suvit

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School of Information Technology

suvit.po@kmitl.ac.th

Now showing 1 - 9 of 9

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The Comparison of Deep Learning Model Efficiency for Classification of Oral White Lesions
(2022-01-01)
Phosri, Kunchidsong
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Treebupachatsakul, Treesukon
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Chomkwah, Wanwalee
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Tanpatanan, Tananan
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Thanathornwong, Bhornsawan
Oral cancer is one of the top health problems globally. Some white lesions of the oral cavity can develop into oral cancer if not screened and treated immediately. Modern screening technologies are popular for applying deep learning knowledge to screen and classify images. In this study, we used deep convolution neural network (CNN) to classify oral white lesions, ulcers, and normal anatomy using transfer learning, which can reduce training time. Ten pre-trained model of transfer learning including DenseNet121, DenseNet169, DenseNet201, Xception, ResNet50, InceptionResNetV2, InceptionV3, VGG16, VGG19, and EfficientNetB7 are implemented and evaluated. The evaluation of accuracy, precision, F1score, recall, sensitivity, confusion matrix, and AUC-ROC curve are discussed. The trained models of DenseNet169, DenseNet201, and Xception showed the highest testing accuracy of more than 90% and recall of 0.8833. In addition to the precision, F1score, and specificity, the DenseNet169 outperforms at 0.9034, 0.884, and 0.9417, respectively.
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Deep Learning to Classify Bacterial Species in the same Genus
(2024-01-01)
Sheela, Sherin
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Piang, May Phu
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Sakorntanant, Sakda
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Poomrittigul, Suvit
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Treebupachatsakul, Treesukon
Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset.
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The Study of Image Quality Effect on Model Performance for Bacteria Classification
(2025-01-31)
Treebupachatsakul, Treesukon
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Chomkwah, Wanwalee
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Tanpatanan, Tananan
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Poomrittigul, Suvit
One of the key requirements for supervised learning in deep learning model construction is the dataset for training and validation. For gathering the dataset, obtaining various image qualities from different resources is unavoidable, and this has been considered to affect the supervised model performance. This research proposes to demonstrate the effect of image quality involving high and standard datasets obtained from 2 different resources on the performance of models. The various cell characteristics with gram-positive and gram-negative bacteria datasets were challenged for trial. These different datasets were matched and contributed to 5 cases; case 1: train and test with high-quality images, case 2: train with high-quality images and test with standard quality images, case 3: train and test with images of standard quality, case 4: train with standard-quality images and test with high-quality images, and case 5: train and test with combining these two image qualities. Pre-trained CNN models were implemented to prove the purpose with and without stratified K-fold cross-validation. The results of retrained models showed that the high-performance models require high-quality datasets obtained from the same resource as the testing set, which yield more than 90% of all performance evaluation metrics when tested on challenging unseen datasets. This study provides valuable insights for building high-performance models that can be applied to automate microbiology diagnostics, impacting public health and clinical practice.
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Microorganism image recognition based on deep learning application
(2020-01-01)
Treebupachatsakul, Treesukon
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Poomrittigul, Suvit
The application of Machine Learning for microorganism, especially bacteria and yeast, recognition becomes attractive because it can reduce the analyzing time of microorganism classification and eliminates human error compare to the classic biological techniques. Therefore, the recognition of microorganism based on Deep Learning increases the efficiency and accuracy of diagnostic process of infected patient. This research studies the possibility to use image classification and deep learning method to recognize bacteria and yeast with the comparison of cell image data-quality between our standard-resolution dataset and high-resolution dataset. We purpose this implementation method of microorganism recognition system using Python programming and the Keras API with Tensorflow Machine Learning framework. The experimental results have shown that bacteria and yeast cell images from microscope are able to be recognized. From the experimental results compare the deep learning methodology of different quality image dataset, our standard resolution dataset could be applied for obtaining more than 80% accuracy of prediction bacteria and yeast.
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A Comparison of Deep Learning CNN Architecture Models for Classifying Bacteria
(2022-01-01)
Poomrittigul, Suvit
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Chomkwah, Wanwalee
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Tanpatanan, Tananan
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Sakorntanant, Sakda
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Treebupachatsakul, Treesukon
Since identifying bacteria from a patient's sample for medical diagnosis purposes by the traditional approach is time-consuming and requires the pathologist's expertise to do the bacteria identification procedure. Thus, involving the deep learning model reported the capability of multi-class image classification allows us to reduce the time and increase the prediction accuracy of the bacteria identification process. This research includes 35 different bacteria species and 6 different Convolutional Neural Network (CNN) architectures. Convolutional Neural Network (CNN) architectures are LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-18, and ResNet-34. The results confirmed the perceptional performance by applying Stratified K-fold cross validation with VGG-16 and observing the multi-class performance with the AUC-ROC score.
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Classification of Depression Audio Data by Deep Learning
(2022-01-01)
Homsiang, Phanomkorn
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Treebupachatsakul, Treesukon
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Kiatrungrit, Komsan
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Poomrittigul, Suvit
Due to many factors such as anxiety from contracting the disease and concern about the socioeconomic impacts, Thai people have accumulated stress and are at risk of depression. The diagnosis of depression can be primarily assessed by testing the assessments such as PHQ8, PHQ-9, and CES-D. The applied deep learning technology in medicine has received research interest and has been developing. In this research, we tried the classification of depression and non-depression audio datasets with the implementation of 4 model architectures: 1D CNN, 2D CNN, LSTM, and GRU. By converting wave audio format (WAV) of Daic-woz database to the Melfrequency cepstrum (MFC). We have done the training and evaluated the 4 model architectures and compared the results between non-augmented and augmented datasets. The highest accuracy was obtained from 1D CNN with a non-data augmentation of 95%, and a 2D CNN with a data augmentation of 75%. These results confirm that human voices can differentiate between depression and non-depression.
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Preprocessing Technique for Oral Lesion Classification using U-NET Segmentation
(2023-01-01)
Dissorn, Pun
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Treebupachatsakul, Treesukon
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Phosri, Kunchidsong
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Thanathornwong, Bhornsawan
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Khovidhunkit, Siribang On Piboonniyom
This research aims to use deep learning techniques to segment oral lesions in medical images for use as a preprocessing step in a classification model. Due to the complexity of oral lesions with undefined margins and dynamic shapes, and the limited amount of data certified by dentists, this approach was found to be underfitting and unsatisfactory. To improve the accuracy of the model, a new approach was proposed to segment interferences such as teeth from the images. This allows the model to better focus on the oral lesions. To achieve this goal, we implemented U-net models with different additional Convolutional Neural Networks (CNN) backbones, including DenseNet 121, EfficientNet B3, VGG 19, ResNet 18, SE-ResNet 18, ResNeXt 50, Inception V3, Mobilenet V2 and SE-ResNeXt 50. A segmentation model was trained with five classes of oral lesions: leukoplakia, pseudomembranous candidiasis, lichen planus, ulcer, and other white lesions. The results showed that DenseUNet and EfficientUNet achieved the highest validation and Intersection over Union (IoU) scores of 98% and 92%, respectively. Our proposed approach effectively segmented the interferences from the images, demonstrating the success of these models in handling the approach. Subsequently, a CNN model of DenseNet 121 was employed for classification. The training accuracy achieved 99.1%, while the validation and test accuracies reached 86.1% and 75.5%, respectively.
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Bilingual Audio Depression Identification Model by Machine Learning
(2025-01-01)
Poomrittigul, Suvit
;
Kiatrungrit, Komsan
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Homsiang, Phanomkorn
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Treebupachatsakul, Treesukon
The number of depression patients worldwide, particularly in Thailand, is increasing on an upward trend. Depression screening commonly relies on self-report questionnaires. However, these instruments provide subjective assessments. Recent advancements in machine learning technology offer potential improvements in diagnostic accuracy through more objective measures. This study aims to evaluate the effectiveness of machine learning models in classifying depression using a bilingual audio dataset comprising Thai and English languages. Such models have the potential to assist clinicians by providing objective preliminary screening for depression based on vocal analysis, enhancing diagnostic precision and clinical decision-making. Various machine learning models were implemented including KNN, MLP, Random Forest, Decision Tree, SGD, Logistic Regression, SVM, AdaBoost, and Gaussian Naïve Bayes using MFCC-converted audio datasets. The results indicate that machine learning models effectively classify and identify depression even in bilingual audio datasets compared to individual language models, with the highest accuracy reaching 0.95 from MLP and KNN when testing the trained model by a single Thai audio.
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Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images
(2025-12-01)
Khovidhunkit, Siribang on Piboonniyom
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Phosri, Kunchidsong
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Thanathornwong, Bhornsawan
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Rungraungrayabkul, Dulyapong
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Poomrittigul, Suvit
Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improving the survival rate of the patients. This study aims to evaluate the computer vision technique in the white oral lesion domain within the scope of photographic images. Deep learning techniques for the classification of Convolution Neural Networks (CNNs) and transformer neural networks, and one-stage models of YOLOv7 and YOLOv8 were utilized to classify and detect five classes of OPMDs and non-OPMDs oral white lesions including oral leukoplakia, oral lichen planus, pseudomembranous candidiasis, oral ulcers covered with pseudomembrane and other white benign oral lesions. From the evaluation results of classification, the IFormerBase model achieves overperformance compared to CNN models with accuracy, precision, and F1 score of more than 80% on the test set. The best model for object detection is YOLOv7 with 84.5% mean Average Precision (mAP) at Intersection over Union (IoU) threshold of 0.3 and 74.5% at IoU of 0.5 on the test set. Object detection results reveal promising automatic oral lesion identification, which can be further developed to enhance the lesion screening system.

Poomrittigul, Suvit

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