Titijaroonroj, Taravichet

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Titijaroonrog, Taravichet

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School of Information Technology

taravichet.ti@kmitl.ac.th

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Automatic Lymph Node Classification with Convolutional Neural Network
(2022-01-01)
Uthatham, Ason
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Yodrabum, Nutcha
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Sinmaroeng, Chanya
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Titijaroonroj, Taravichet
Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.
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Data augmentation based on multiscale radon transform for seven segment display recognition
(2020-01-01)
Popayorm, Sorawee
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Titijaroonroj, Taravichet
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Phoka, Thanathorn
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Massagram, Wansuree
To alleviate the problem of limited data in creating rotation, scale, perspective, and illumination invariant of the neural network training sets, the multiscale Radon transform is proposed in this study to enhance the data augmentation for seven segment display recognition. Resizing, smoothing, and coefficient shifting generate the desired invariant effects for the training model. The accuracy rates from the experiment demonstrate the superiority of the proposed method over other data augmentation techniques with the best overall accuracy performance of 87.05%-outperforming other data augmentation techniques by 6-13%. The convolutional neural network model generated from the proposed multiscale Radon transform data augmentation is suitable for seven segment display recognition and could become beneficial to other type of self-luminous type of images.
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Text-background decomposition for thai text localization and recognition in natural scenes
(2014-01-12)
Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Suttapakti, Ungsumalee
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Boonchukusol, Pimlak
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Titijaroonroj, Taravichet
Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-en-tropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.
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Automatic Thai Ticket Classification By Using Machine Learning For IT Infrastructure Company
(2022-01-01)
Khowongprasoed, Kraidet
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Titijaroonroj, Taravichet
Ticket classification is a process to define the category name of each ticket before assigning the resolution team to serve each ticket. It is an important process to support the customers inside and outside the company. It can make customer dissatisfaction if the processing time is high or delayed. Based on the recording data in 2019-2021 at the studying company, we found that the manual ticket classification got an error rate about 53 percent because the office workers misunderstand. To alleviate this problem, we propose the methodology for automatic Thai ticket classification by using Term Frequency-Inverse Document Frequency with Support Vector Machine. The experimental result shows that the performance of the proposed methodology is higher than the manual classification by 2 times or 41 percent.
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Chertify: Wood Identification-Based Mobile Cross-platform by Deep Learning Technique
(2022-01-01)
Wongpoo, Teerasak
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Sriwan, Wannamongkol
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Titijaroonroj, Taravichet
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Jamsri, Pornsuree
Thailand’s economic trees are counted as one of its most valuable domestic assets and well known internationally as a high quality natural wood resource. However, there is a need for basic wood identification whether or not for a required certificate by individuals, entrepreneurs, and organizations. Currently, the wood identification process is manually accomplished only by an expert at the Forest Research and Development Office, the Royal Thai Forest Department. This is a time consuming complex process for two reasons–required experience and limited experts. Given the complexity of wood identification, a new approach is offered, namely, to identify different types of wood with an image from a smartphone. The researcher initially proposes a mobile application, “Chertify”, that has five features (login, wood check, wood check history, manual, and wood knowledge). This app can serve both iOS and Android platforms and targets the general user. Chertify aims to simplify identification of an economic wood type by combining deep learning technology with an actual wood image on a Smartphone. The selected deep learning algorithm will be applied to 258 trained group images of seven wood types based on highest accuracy and lowest standard deviation. Chertify relies on a handcrafted method (HOG and SVM) and a learning-based method (Alexnet) with accuracy of 69.4% and 84.73% and SD at 5.37% and 3.07% values, respectively.
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Drugtionary: Drug Pill Image Detection and Recognition Based on Deep Learning
(2022-01-01)
Pornbunruang, Naphat
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Tanjantuk, Veerapong
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Titijaroonroj, Taravichet
Drugtionary, which is a mobile application, is developed to support people who lack medical understanding and avoid taking the wrong drug. It consists of four main features including (i) sign-up, (ii) managing profile and medication history, (iii) viewing medication information, and (iv) managing the schedule. For viewing medication information, there are three ways to retrieve the drug information–(i) text search, (ii) chatbot, and image search. We use string search and DialogFlow for text search and chatbot, respectively, whereas deep learning technique for image detection and recognition is used to search the given drug pill image. The experimental result shows that the model generated from the CenterNet method is suitable when compared to the Faster-RCNN, RetinaNet, Yolo, and SSD on our drug pill dataset. Moreover, our application is constructed by using React and React Native technology. All data are stored in the MongoDB database.
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Proposed Structural Validation-based Testing for Object-Oriented Programming
(2021-01-21)
Lumyong, Chayanin
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Tachasriburapha, Natthawut
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Titijaroonroj, Taravichet
Nowadays, Java program testing is widely used in an academic e-learning system. It can help a lecturer to give feedback and suggestion to learners automatically and immediately. Especially, an object-oriented programming (OOP) class should have a system to support learners. It is difficult and labor-intensive, if there are many learners. The test case-based approach is widely used in programming testing, because they are simple and convenient. This approach requires input-output pairs to evaluate the submitted code by comparing the output from their code with the expected output. However, it considers the output only, thus making it not appropriate for an OOP class. To develop the Java program testing to support an OOP class, a proposed structural validation-based testing (PSVT) is proposed. The experiments showed that our method can evaluate the correctness and the relation validity of the given code which is corresponding to the human. Moreover, its complexity is O(N^{2}).
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Blood Vessels Detection by Regional-based CNN for CT Scan of Lower Extremities
(2022-01-01)
Sakunpaisanwari, Littikrai
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Yodrabum, Nutcha
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Sirirapisit, Tanongchai
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Titijaroonroj, Taravichet
Blood vessels on computed tomography (CT) scan images are difficult to identify and discriminate between vessels and noise because blood vessels are not only small and shapeless, but its location can also be inconsistent. This is a challenge of object detection. We proposed an automatic blood vessel detection method based on YOLOv3 for object detection from CT scan of lower extremities. This work focused on detecting four main arteries: popliteal, anterior tibial, posterior tibial, and peroneal arteries. To obtain the best architecture for blood vessel detection, we evaluated and compared the performances of seven region-based CNN architectures: Faster R-CNN, Cascade R-CNN, Mask R-CNN, RetinaNet, YOLOv3, CornerNet, and Centernet. Experimental results show that the best architecture was YOLOv3 with precision, recall, and f1-score of 0.982, 0.954, and 0.968, respectively. Good accomplishment of YOLOv3 came from skip connections, multi-scale feature map, and anchor generated by k-means clustering.
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Automated Verification of English Proficiency Test Scores Using OCR for Graduation Qualification
(2025-01-01)
Titijaroonroj, Taravichet
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Maliwan, Thitiwut
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Jaimetha, Nattakamon
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Wattanacheep, Bhattarabhorn
An algorithm is proposed to verify English proficiency scores using OCR for graduation qualification, aiming to reduce errors and workload in the current manual verification process of officer. Score reports in image or PDF format, including TOEIC and KMITL-TEP, are processed using two deep learning-based document classifiers that identify the test type and score type prior to OCR execution. Preprocessing techniques such as noise reduction, contrast enhancement, and skew correction are applied to improve OCR accuracy. Three OCR models-Tesseract, TrOCR, and EasyOCR-are evaluated for text extraction performance. The extracted textual data are then converted into structured JSON, enabling automated rule-based comparison against graduation criteria. Evaluation performance is measured using Accuracy, F1-Score, Character Error Rate (CER), and Word Error Rate (WER). Experimental results show that the proposed system achieves 99% accuracy, demonstrating both high reliability and adaptability to institutional scoring standards. The integration of OCR significantly reduces processing time while maintaining flexibility to accommodate future changes in assessment policies.
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An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects
(2019-07-01)
Hancherngchai, Kangsadan
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Titijaroonroj, Taravichet
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Rungrattanaubol, Jaratsri
Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. The PCA consists of two main steps including (I) covariance matrix calculation and (II) eigenvector and eigenvalue extraction. In case of face recognition, the input image is converted to the vector form before forwarding to covariance matrix computation. Then, the matrix is used to extract the eigenvector and eigenvalue. Two-dimensional PCA (2DPCA) is introduced to reduce high-dimensional problems. The illumination effect problems in the face recognition is still needed to be resolved. In order to improve and solve the problems, this paper proposes an individual local mean-based 2DPCA (ILM-2DPCA), which replaces a single local mean in 2DPCA method. The individual local mean can provide more appropriate mean to each image, which can reduce the illumination effect effectively. The experimental study is set up on dataset Yale face database B+. The results indicate that the proposed method outperforms, based on the accuracy rate, all the baseline methods which are 2DPCA, I-2DPCA, Bi2DPCA and 2D<sup>2</sup>PCA.

Titijaroonroj, Taravichet

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