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Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
(2026-12-01)
Yodrabum, Nutcha
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Wongpraparut, Chanisada
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Titijaroonroj, Taravichet
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Chularojanamontri, Leena
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Bunyaratavej, Sumanas
Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.
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SkinHRNet: A Deep Learning Framework for Non-Contact Heart Rate Estimation and Arterial–Venous Sufficiency Status Classification from Short Skin Videos
(2026-01-01)
Traivinidsreesuk, Chetsadaporn
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Yodrabum, Nutcha
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Winaikosol, Kengkart
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Chaikangwan, Irin
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Prompattanapakdee, Jiraya
Traditional methods for monitoring flap health in reconstructive surgery are often invasive and rely on subjective assessment. This study addresses clinically motivated monitoring challenges by evaluating two tasks using a dataset of 1,018 short skin videos: average heart rate (HR) estimation under arterial–venous sufficiency conditions and arterial–venous sufficiency status classification across sufficiency and simulated insufficiency conditions. To address these challenges, we propose SkinHRNet, a deep learning–based approach for average HR estimation and arterial–venous sufficiency status classification from short skin videos. This work contributes a short-skin-video framework that combines HR-related signal estimation with arterial–venous sufficiency classification to support the two evaluated tasks under the controlled conditions considered in this study. For average HR estimation under arterial–venous sufficiency conditions, SkinHRNet achieved a mean absolute error (MAE) of 8.66 ± 4.85 BPM. For arterial–venous sufficiency status classification, it achieved an accuracy of 0.969 ± 0.02 across the evaluated sufficiency and simulated insufficiency conditions. These findings indicate that SkinHRNet may serve as an initial research prototype for further investigation of short-video-based non-contact assessment under controlled arterial–venous sufficiency and simulated insufficiency conditions.
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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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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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Seven Segment Display Detection and Recognition via Deep Learning Technique
(2022-01-01)
Suttapakti, Ungsumalee
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Titijaroonroj, Taravichet
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Nunsong, Walairach
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Kakanopas, Donyarut
Seven segment display detection and recognition play an important role in determining the status of manufacturing machines. However, in some industrial factories, employees are still assigned to manually record the status of the seven segment displays. This is not real-Time tracking status and it is easy to make the typos or mistakes while collecting data. Hence, image processing and machine vision are used to automatically detect and recognize images from the seven segment displays. In this paper, the Cascade R-CNN is applied to automatically detect and recognize seven-segment displays in a single model-End-To-end learning because this method is efficient and flexible. The Cascade R-CNN method achieves precision, recall, and F1-score of 0.999 which are higher than conventional methods and the state-of-The-Art methods, including Faster R-CNN, RetinaNet, NAS-FPN, CornerNet, and CenterNet. Although the recognition accuracy of Cascade R-CNN is slightly lower than those of YOLOv3 and CornerNet, its accuracy is still higher than the Faster R-CNN, SSD, RetinaNet, NAS-FPN, and CenterNet. This method can automatically detect and recognize the digits on seven-segment display in a single model, thus improving the effectiveness for detecting and recognizing seven-segment display images.

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