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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Scientific Journal of Engineering Research
Nguyen, Van-Tuan
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Computer Science & IT Engineering

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Classification of upper gastrointestinal tract diseases using endoscopic images Tran, Thanh Hai; Nguyen, Van-Tuan; Dao, Viet-Hang; Nguyen, Phuc-Binh; Nguyen, Thanh-Tung; Vu, Hai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp833-842

Abstract

Automatic classification and disease detection in medical images, aided by machine learning, provide crucial support to prevent overlooked instances and ensure prompt treatment of diseases. Despite impressive achievements in the field of polyp detection from endoscopic images, classification of other diseases, such as reflux esophagitis, esophageal cancer, gastritis, gastric cancer, and duodenal ulcer, is still subject to significant limitations and remains a challenging area of study because of their different and more challenging characteristics. This paper proposes a method to roughly classify the diseases from the whole images by deep learning. In particular, we focus on identifying hard samples from the training dataset and enriching them with some fundamental augmentation techniques. We then employ a cutting-edge model, specifically ResNet, for the final classification stage. Additionally, we enhance the original ResNet’s loss function by incorporating another loss function called focal loss. These modifications play a crucial role in boosting the accuracy of the ResNet model. Our proposed method outputs the disease category and corresponding heat map showing the area of interest. It achieved very promising accuracy (99.55%) for the classification of five lesions on our self-collected dataset. It serves a dual purpose. Firstly, it aids in the training of novice endoscopists, enabling them to gain valuable experience. Secondly, it offers a rapid solution for annotating extensive volumes of endoscopic image data at the label level.
Integrated Vision-PLC Control Architecture for High-Performance Delta Robot Sorting in Industrial Automation Vo, Kim-Thanh; Nghia, Bui-Duc; Tran, Huy-Vu; Huynh, Thanh-Tuan; Nguyen, Huy-Bao; Nguyen, Phong-Luu; Nguyen, Van-Tuan; Phan, Anh-Quoc; Phung, Son-Thanh; Nguyen, Van-Dong-Hai; Nguyen, Binh-Hau; Nguyen, Van-Hiep; Nguyen, Thanh-Binh
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.337

Abstract

The rapid development of automation and robotics has increased the demand for high-performance industrial systems, in which Delta robots play a crucial role due to their lightweight structure, high speed, and precise positioning capability. This study aims to design, implement, and evaluate a Delta robot-based product classification system integrating PLC S7-1200 control and Machine vision. The proposed system employs a camera to detect object shape, color, and position on a conveyor, while a PC processes the image data and computes the robot’s inverse kinematics before transmitting control commands to the PLC. A hardware model of the Delta robot was designed and fabricated, and a dual-mode control application was developed to monitor and operate the robot in real time. Experimental results demonstrate that the system achieves stable operation, with a classification speed of up to 20 products per minute and an accuracy of approximately 95.7% for picking and placing tasks. The findings confirm the feasibility and effectiveness of integrating vision-based detection with high-speed parallel robot control for industrial sorting applications. The study also provides a foundation for further optimization in processing speed, mechanical design, and advanced image-processing techniques to enhance system performance in practical manufacturing environments.
Co-Authors Dao, Viet-Hang Huynh, Thanh-Tuan Nghia, Bui-Duc Nguyen, Binh-Hau Nguyen, Huy-Bao Nguyen, Phong-Luu Nguyen, Phuc-Binh Nguyen, Thanh-Binh Nguyen, Thanh-Tung Nguyen, Van-Dong-Hai Nguyen, Van-Hiep Phan, Anh-Quoc Phung, Son-Thanh Tran, Huy-Vu Tran, Thanh Hai Vo, Kim-Thanh Vu, Hai