Tungjitkusolmun, Supan

Preferred name

Alternative Name

Tungjitkusolmun, S.

Main Affiliation

School of Engineering

supan.tu@kmitl.ac.th

Now showing 1 - 10 of 74

Some of the metrics are blocked by your
consent settings
Basic investigation of breast cancer detection in early stage using microwave radiation: Finite element analysis approach
(2011-12-01)
Sanpanich, A.
;
Phasukkit, P.
;
Tungjitkusolmun, S.
;
Pintavirooj, C.
;
Wongtrairat, W.
One of the main problem which threatening health in middle-aged women around the world is breast cancer. Not only a physical painfulness but also including her own family and victim relatives who suffering from this unavoidable pity fate. To detect those cancerous tumors as in an earlier stage as possible seem to be the best way to handle with this hazard. Besides from x-rays mammography and a conventional diagnostic ultrasound, microwave radiation tomography technique is a promising method to investigate any abnormality forming in her mammalian gland. In this paper, we propose a simulation of microwave radiation using a finite element method (FEM) of our modified imaging setup system for female breast cancer diagnosis. By using FEM, we propose a preliminary study of microwave breast cancer detection from a patch antenna propagation in 3D tissue space approaching. In term of wave penetration, voluminous 3D distribution geometry of electrical field in nonhomogenous simple breast phantom model was presented. These simulations not only show a promising result but also encourage us to develop a real imaging setup system in the near future. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
Temperature Distribution of Microwave Balloon Treatment Benign Prostatic Hyperplasia using Finite Element Model
(2019-11-01)
Jandang, Sinchai
;
Tungjitkusolmun, Supan
;
Phasukkit, Pattarapong
In this paper, the 3D model of Microwave balloon antenna for treatment urethral stricture from Benign Prostatic Hyperplasia (BPH) was presented. The 3D modeling heating pattern's ability of thin slot antenna of microwave balloon in the research was also demonstrated. As the results showed that the distribution of temperature and surrounding temperature of modeling of open microwave system. Microwave system at 2.45 GHz. Power Operation are 40, 60 and 80 W. Time operation are 5-600s. A design microwave balloon antenna has protection of critical tissue structure.
Some of the metrics are blocked by your
consent settings
Combined medical imaging with CFD analysis for application of diagnostic and treatment planning: The case study of human nasal airflow simulation based on CT imaging
(2009-01-01)
Hemtiwakorn, Khaisang
;
Phoocharoen, Niwat
;
Tungjitkusolmun, Supan
;
Mahasittiwat, Visan
;
Sangworasil, Manas
The previous methods of human airflow measurement such as rhinomanometry or acoustic rhinometry could not visualize the airflow and calculated the velocity magnitude in specific region of nasal cavity. Thus, this study proposes the combination of CT and CFD analysis of nasal airflow simulation in normal human breathing. This method can demonstrate the airflow direction, and also calculate the velocity magnitude inside the nasal cavity. The processes of this study are segmentation, meshing, solving, and post-processing. Firstly, Mimic 10.01 software was used to segment the nasal cavity. Secondly, Pro-STAR/amm software was used to generate trimmed mesh of 338, 496 elements. Finally, computational grid model was imported to STAR-CD version 3.26 for numerical computation, and visualize the solution by post-processing. The result shows that velocity magnitude of airflow is greatest in nasal valve area which is the narrowest area of the human nose. Also, the flow commonly pass through the main nasal passage and middle meatus areas of nose. Experimental validation will be reported in the further publication. In conclusion, this study is the important begining of an applied CFD analysis with medical imaging. CT imaging combines with CFD analysis could be useful for rhinologist as a nose function evaluation technique. Moreover, the applications of this method could be utilized in a wide range of research topics, especially development of diagnostic and treatment planning techniques.
Some of the metrics are blocked by your
consent settings
Computational fluid analysis of blood flow characteristics in abdominal aortic aneurysms treated with suprarenal endovascular grafts
(2009-12-01)
Sun, Zhonghua
;
Chaichana, Thanapong
;
Sangworasil, Manas
;
Tungjitkusolmun, Supan
The purpose of this study is to investigate the hemodynamic effects of suprarenal stent grafts on the renal arteries in the treatment of patients with abdominal aortic aneurysm. Suprarenal stent grafts have been increasingly used for treatment of aortic aneurysms with suboptimal aneurysm necks. However, the long-term safety of this procedure is yet to be determined. 2 sample patients undergoing suprarenal stent grafting were included in the study. Four variable configurations of stent wires crossing the renal artery ostium were simulated in aorta models based on CT generated data, at different cardiac cycles. The stent wires thickness was set 0.5 mm which is similar to the actual diameter of a stent wire. Computational fluid dynamic analysis showed slightly decrease of flow velocity to the renal arteries with multiple wires crossing, and no changes of flow velocity to the renal arteries with single wire crossing. Our preliminary results demonstrated the safety of suprarenal stent grafting. Further studies are required to investigate the effect of different wire thickness on subsequent hemodynamic changes to the renal arteries.
Some of the metrics are blocked by your
consent settings
Mechanical compliance of the endocardium
(2002-12-01)
Bin Choy, Young
;
Cao, Hong
;
Tungjitkusolmun, Supan
;
Tsai, Jang Zern
;
Haemmerich, Dieter
Radio-frequency (RF) ablation is an accepted treatment for cardiac arrhythmias related to abnormal focal cardiac substrate. The penetration depth of the electrode into the endocardium affects lesion size, a critical determinant of success of RF ablation. We measured the relation between the mechanical compliance and the penetration depth of RF ablation catheter electrode at frequently ablated areas of the endocardium and examined the influence of time after death on mechanical properties of the tissue. We measured force versus time for eight insertion depths of the catheter electrode into full-thickness endocardial samples derived from the mitral valve annulus, the left ventricular free wall and the tricuspid valve annulus. We varied the time after death at 15, 40min, 3, 8, and 18h and repeated our measurements. At 15min after death, the first 0.5mm penetration depth caused the fastest relaxation at 55s. Force decay decreased dramatically at 15min after death as the penetration depth increased from 0.5 to 4mm. We used the force data sampled at 60s after insertion to approximate the elasticity. We observed the relations between the force versus the insertion depth. The force increased by a factor of 5 for the mitral valve annulus and 8 for the left free wall from 15min to 18h. We derived coefficients of a second-order polynomial equation relating the force data to insertion depth with R<sup>2</sup>>0.99. © 2002 Elsevier Science Ltd. All rights reserved.
Some of the metrics are blocked by your
consent settings
Real-time monitoring glucose by used microwave antenna apply to biosensor
(2011-12-01)
Wiwatwithaya, Sujitra
;
Phasukkit, Pattarapong
;
Tungjitkusolmun, Supan
;
Wongtrairat, Wannaree
In this paper we investigate the electromagnetic field interaction with a glucose aqueous solution using a microwave antenna (U-shape) to evaluate the glucose concentration and vary temperature. The glucose concentration vary from 10-40 mg/ml compare with DI(de-ionized) water, the operating frequency of about 1-2.5GHz. The change of the glucose concentration is directly related to the change of the reflection coefficient due to electromagnetic interaction between the dielectric wave and the glucose aqueous solution. A glucose biosensor using microwave antenna (U-shape) provides a unique approach for glucose monitoring. The antenna is designed have various formed and test by comsol program. The principles of Comsol is finite element for dispersion of electromagnetic waves from a real experiment to measure the concentration of glucose solution. © 2011 IEEE.
Some of the metrics are blocked by your
consent settings
Classification of magnetic resonance images using support vector machines
(2001-12-01)
Sookpotharom, Supot
;
Tungjitkusolmun, Supan
;
Airphaiboon, Surapan
;
Sangworasil, Manas
The classification of medical images obtained from magnetic resonance imaging (MRI) is an important step in the visualization of soft issue in the human body. MRI is a multidimensional technique as it provides information about three tissue dependent parameters. This paper presents the potential of Support Vector Machines (SVMs) technique for the supervised classification of MRI images. The SVMs approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification are used, in particular we consider binary trees of SVMs to solve the multiclass of MR brain images. The experiments using the SVMs technique presented in this paper performed the quality and correctly position of the internal organ.
Some of the metrics are blocked by your
consent settings
An Infant Cry Recognition based on Convolutional Neural Network Method
(2019-11-01)
Teeravajanadet, K.
;
Siwilai, N.
;
Thanaselanggul, K.
;
Ponsiricharoenphan, N.
;
Tungjitkusolmun, S.
In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.
Some of the metrics are blocked by your
consent settings
Crack Localization Detection in Monolithic Zirconia Dental Crowns via 1D-Convolutional Neural Networks Algorithm-Based Acoustic Emission Analysis
(2024-01-01)
Tuntiwong, Kuson
;
Wangman, Rangsinee
;
Sritart, Hiranya
;
Kanchanatawewat, Kanchana
;
Tungjitkusolmun, Supan
The feasibility of utilizing the acoustic emission (AE) technique for the detection and classification of cracks within monolithic dental crown is assessed in this study, owing to its non-destructive nature which enables passive monitoring of structures. The AE signals captured are subjected to analysis to extract pertinent information regarding the source and location of the cracks. A novel approach is proposed, employing deep learning 1D convolutional neural networks (1D-CNNs) for the recognition and classification of the recorded cracked signals. The AE signals, obtained through a handmade AE data acquisition unit, are converted into .csv format and subjected to denoising using Bayesian methods to eliminate background noise. The signals are collected through the breakage of pencil lead (Vallen systeme) Hsu-Nielsen-Source 0.5 (ASTM E976) applied to each surface of the dental crown. Subsequently, the data signals are divided into training and testing groups following an 85 / 15 split. The performance of the deep learning 1D-CNNs is evaluated based on Precision, Recall and total accuracy metrics. The applicated of automated deep learning in this study demonstrated significantly high overall accuracy (98.67%). The integration of handmade data acquisition with 1D-CNN crack detection proves to be an effective method for early screening. The novel method harnesses acoustic emission signals in 1D-CNNs, thereby enhancing the accuracy of clinical dental restorative crack identification and determining the onset time.
Some of the metrics are blocked by your
consent settings
Cross-Sensor and Cross-Population Generalization of Deep Learning Models for Digital Mammography: A Controlled Four-Country Benchmark of Five Backbone Architectures with Statistical Significance Testing
(2026-06-01)
Gabbualoy, Somprasonk
;
Phasukkit, Pattarapong
;
Tungjitkusolmun, Supan
Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license.

Tungjitkusolmun, Supan

Filters

Settings

Sort By

Results per page