Tungjitkusolmun, Supan
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Tungjitkusolmun, Supan
Alternative Name
Tungjitkusolmun, S.
Main Affiliation
Email
supan.tu@kmitl.ac.th
6 results
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Item type:Publication, Message from Technical Program Chair(2023-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated segmentation of infarct lesions in t1‐weighted mri scans using variational mode decomposition and deep learning(2021-03-02); ; ; Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1‐weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non‐infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep‐learning‐based segmentation task. Finally, a three‐dimensional U‐Net model is developed to perform patch‐wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset are utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest(2020-04-01); ; ; The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning Convolutional Neural Networks (CNNs) on Recognition and Classification of White Blood Cells (WBCs)(2023-01-01); The rapid evolution of Artificial Intelligence has brought significant advancements in Deep Learning, a widely applied subfield across various industries, including healthcare. This study focuses on leveraging Deep Learning and image processing techniques to classify white blood cells (WBCs). By comparing and evaluating multiple convolution neural network (CNN) models, such as GoogLeNet, ResNet50, VGG16, and Squeezenet, including YOLO (You Only Look Once) algorithm known for its segmentation capabilities, the objective is to improve the accuracy and efficiency of WBC recognition and classification. During training, both VGG16 and ResNet50 models achieved the highest accuracies, with VGG16 at 97.70% and ResNet50 at 97.38%. However, ResNet50 was chosen as the preferred model to be utilized alongside YOLO for improved classification and segmentation advancements. The testing phase of ResNet50 utilized 10% of the dataset from the initial data and demonstrated over 90% accuracy in each class. The process showed higher efficiency and lower capital costs, making it suitable for medical applications aided by Deep Learning Convolutional Neural Networks, addressing limitations in white blood cell classification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reconstruction of 3D Abdominal Aorta Aneurysm from Computed Tomographic Angiography Using 3D U-Net Deep Learning Network(2022-01-01); (1) Background: An abdominal aortic aneurysm (AAA) is a swelling (aneurysm) of the aorta that occurs when the wall of the aorta weakens. An AAA is a potentially life-threatening condition, especially if it eventually ruptures, causing severe bleeding. (2) Methods: We developed an automated segmentation method for 3D AAA reconstruction from computed tomography angiography (CTA) based on the 3D U-NET deep learning network approaches for AAA and AAA with thrombus on training dataset classified as 8 normal, 14 aneurysm volume, and 38 thrombus aneurysm volume with the data augmentations app, i.e., scaling, random crop, grayscale variation, axial y flip, and shear, were added to the training model, achieving better performance. (3) Results: The results confirm that the proposed method can provide accuracy in terms of the Dice Similar Coefficient (DSC) scores of 0.9669 for training performance and 0.9868 for testing evaluation with the 3D U-Net model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Advanced White Blood Cells Detection and Analysis with VGG16 Transfer Learning(2024-01-01); White blood cells (WBCs) provide a significant role in the immune system, and precise classification and quantification are critical for detecting a variety of diseases. This work employs the VGG16 model combined with transfer learning to improve WBC classification and counting in blood smear pictures. We utilized a dataset of 1,651 images, including samples from normal bone marrow and chronic myeloid leukemia (CML) cases, to train and evaluate the model. The workflow incorporates data augmentation, Otsu thresholding, and morphological operators to improve segmentation accuracy. The VGG16-based model achieved a high accuracy of 92.37%, with validation accuracy reaching 96.41 %. Performance metrics were evaluated using accuracy, precision, recall, and F -measure, highlighting that eosinophils provided the highest accuracy, while neutrophils demonstrated the best precision. The model effectively distinguishes between normal and elevated WBC counts, as evidenced by the results from normal and CML blood smears. Despite these promising results, further refinement is suggested. Future work may focus on specific WBC types, such as monocytes or neutrophils, to improve precision and balance memory efficiency. This approach has the potential to advance diagnostic accuracy and efficiency in medical image analysis.