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Development of a Deep Learning-Based System for Cat Skin Disease Classification and Grad-CAM Visualization

Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 339-344页

作者： Won, Hyeong-Sik Cho, Hyun-Chong Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of

Skin diseases in companion cats can worsen if not treated promptly, and this can increase the financial burden on pet owners. To prevent this, early and accurate diagnosis is essential. This study introduces a deep learning-based Computer-Aided Diagnosis (CADx) system designed to classify cat skin diseases into non-inflammatory and inflammatory lesions by comparing them with normal images. The system employs the EfficientNetV2 model and incorporates image augmentation techniques like AutoAugment and AugMix to enhance classification performance. The study results indicate that the developed model achieved an 84.68% accuracy for non-inflammatory lesions, reflecting a 10.06% improvement, and a 97.19% accuracy for inflammatory lesions, reflecting a 2.45% improvement. Furthermore, we applied Grad-CAM to visualize the regions of interest in the images, offering veterinarians critical insights into the location and characteristics of the lesions. This system has the potential to significantly improve the precision of diagnosing skin diseases in companion cats, thereby supporting better veterinary care. Copyright © The Korean Institute of Electrical Engineers.

关键词： Veterinary medicine

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A Study on Optimization and Augmentation Techniques for Improving the Performance of CADx System for Cat Skin Disease

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 135-141页

作者： Won, Hyeong-Sik Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatment of skin diseases. Skin diseases are among the common ailments in pets, and swift action is required if they occur in cats. However, early lesions lack distinctive characteristics, making accurate diagnosis difficult. Therefore, this study proposes a CADx(Computer-Aided Diagnosis) system that classifies images of cat skin conditions into inflammatory lesions, non-inflammatory lesions, and normal images using a dataset of cat skin diseases. We selected the ConvNeXt model, based on CNN, which can learn regional information and features. To learn various patterns of skin lesions, we applied Mixup, an image augmentation technique. When Mixup was applied, the model accuracy was 0.8679, showing a 3.35% improvement compared to the original dataset. Additionally, Lookahead Optimizer was applied to ensure stable learning of Mixup. When both Mixup and Lookahead Optimizer were applied, the model accuracy was 0.8741, showing a high improvement of 3.97% compared to the original dataset. Therefore, the Lookahead Optimizer helps with the stable training of mixup, which can improve the performance of the model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Dermatology

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Development of a Deep Learning-Based Model for Canine Skin Disease Diagnosis Using RandAugment and Patterned-GridMask

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 142-148页

作者： Kim, Min-Jun Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In contemporary society, pets are increasingly regarded as integral family members, contributing significantly to human quality of life. The growing prevalence of dog ownership has concurrently escalated the economic burden associated with veterinary care, particularly in managing common conditions like skin diseases. This study introduces an advanced deep learning-based diagnostic system for canine skin diseases, designed for practical application in home environments. We employed RandAugment to enhance data augmentation, thereby increasing the diversity of the training dataset. Furthermore, the implementation of Patterned-GridMask significantly improved the model's generalization capabilities. The use of the AdamW optimization algorithm was instrumental in mitigating overfitting, thus enhancing the model's overall learning efficiency. The proposed Transformer-based model, ViT/B-16, achieved an accuracy of 78.65% with the original dataset. With the integration of RandAugment and Patterned-GridMask techniques, the model's accuracy improved to 84.33%, underscoring its potential effectiveness for practical veterinary applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： medical imaging

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Exploring the mechanistic role of epidermal growth factor receptor activation in non-cancer kidney disease

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BIOCELL 2025年第1期49卷 79-92页

作者： JU-YEON LEE DAEUN MOON JINU KIM Medical Course College of MedicineCatholic Kwandong UniversityGangneung25601Republic of Kore Department of Anatomy College of MedicineJeju National UniversityJeju63243Republic of Korea Interdisciplinary Graduate Program in Advanced Convergence Technology&Science Jeju National UniversityJeju63243Republic of Korea

The epidermal growth factor receptor(EGFR)is a transmembrane glycoprotein that plays a crucial role in signal transduction and cellular *** review explores the function of EGFR in kidney physiology and its implications for various kidney *** signaling is essential for kidney function and repair mechanisms,and its dysregulation significantly impacts both acute and chronic kidney *** review discusses the normal distribution of EGFR in kidney tubular segments,the mechanism of its activation and inhibition,and the therapeutic potential of EGFR-targeting antagonists and ***,it explores the pathophysiological characteristics observed in rodent models of kidney diseases through pharmacological and genetic inhibition of EGFR,highlighting therapeutic challenges and limitations such as species differences,variability in disease models,and potential adverse ***,the findings underscore the multifaceted role of EGFR in kidney diseases,influencing inflammation,fibrosis,and tissue *** complex involvement suggests that targeting EGFR may be a beneficial therapeutic strategy for managing these conditions,potentially mitigating inflammation and fibrosis while promoting tissue repair.

关键词： Epidermal growth factor receptor Chronic kidney disease Acute kidney injury Tubulointerstitial fibrosis

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CyclicAugment: Optimized medical Image Analysis via Adaptive Augmentation Intensity

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IEEE Access 2025年 13卷 86562-86575页

作者： Min-Jun Kim Jung-Woo Chae Hyun-Chong Cho Department of Data Science Kangwon National University Chuncheon-si Republic of Korea Department of Electronics Engineering Kangwon National University Chuncheon-si Republic of Korea Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Chuncheon-si Republic of Korea

Computer-aided diagnosis (CADx) systems play a crucial role in accurately diagnosing and monitoring diseases through medical imaging. However, there are many challenges, such as data scarcity and complex structural patterns, limiting the performance of deep-learning models. Although conventional data-augmentation techniques can help mitigate these limitations, excessive augmentation may distort critical diagnostic information, leading to suboptimal performance. To address this issue, this study proposes CyclicAugment, a novel data-augmentation strategy that dynamically adjusts augmentation intensity in a cyclic manner throughout training. The method gradually transitions from using original data as weak augmentation, subsequently applying strong augmentation before reverting to weak augmentation again, reintroducing the original data. This process ensures stable training through an adaptive learning rate. By cyclically adjusting augmentation intensity, our approach enhances data diversity while preserving essential diagnostic information, thereby improving the generalizability of deep learning models. We validated CyclicAugment across six different medical imaging datasets and multiple deep learning architectures, including convolutional neural networks, vision transformers, and multilayer perceptrons. Our framework achieved a maximum accuracy improvement of 8.8%, reaching a peak performance of 0.927 and thereby demonstrating its effectiveness in enhancing medical image analysis.

关键词： Biomedical imaging medical diagnostic imaging Accuracy Data augmentation Training Computed tomography Lesions Deep learning Imaging Data models

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KNU-DTI: KNowledge United Drug-Target Interaction prediction

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Computers in Biology and Medicine 2025年 189卷 109927-109927页

作者： Heo, Ryong Lee, Dahyeon Kim, Byung Ju Seo, Sangmin Park, Sanghyun Park, Chihyun Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Gangwon-do Chuncheon-si24341 Korea Republic of Department of Data Science Kangwon National University Korea Republic of UBLBio Corporation Yeongtong-ro 237 Gyeonggi-do Suwon16679 Korea Republic of Department of Computer Science Yonsei University Yonsei-ro 50 Seodaemun-gu Seoul03722 Korea Republic of Department of Computer Science and Engineering Kangwon National University Korea Republic of

Motivation: Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks. Results: We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases. © 2025

关键词： Prediction models

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Independent and additive effects of binge drinking and obesity on liver enzymes:a cross-sectional analysis using the Korean National Health Insurance Service data

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Gastroenterology Report 2024年第1期12卷 11-18页

作者： Anthony Kityo Sang-Ah Lee Department of Preventive Medicine School of MedicineKangwon National UniversityGangwonRepublic of Korea Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National UniversityGangwonRepublic of Korea

Background:Binge drinking(BD)has been associated with elevated liver enzymes,but the joint association of BD and adiposity with liver enzymes is *** aimed to examine the combined association of BD and obesity with elevated liver ***:Data were obtained from 285,600 patients in the Korean National Health check-up program during *** I BD(BD I)was defined as alcohol consumption of>60g(men)or>40g(women)on one occasion in the previous ***-intensity BD(HIBD)corresponded to at least two times the BD I *** and abdominal obesity were defined by body mass index and waist *** regression was used to examine the independent and joint associations of BD and obesity with elevated alanine aminotransferase(ALT),aspartate aminotransferase(AST),and gamma-glutamyl transferase(GGT)*** excess risk(RERI),attributable proportion(AP),and synergy index(SI)were calculated to estimate the additive interaction ***:The mean age was 42.1±0.03years and 50.2%were *** ALT[odds ratio(OR)1.09,95%confidence interval(CI)1.02-1.16],AST(OR 1.16,95%CI 1.11-1.23),and GGT(OR 1.84,95%CI 1.05-1.94)were associated with *** odds of elevated ALT(OR 3.57,95%CI 3.43-3.71),AST(OR 3.47,95%CI 3.37-3.58),and GGT(OR 2.10,95%CI 1.98-2.12)were observed in individuals with general obesity.A similar trend was observed for abdominal *** RERI,AP,and SI for the interaction effect of BD and general obesity were 23%,7%,and 13%for elevated AST levels,and 67%,24%,and 58%for elevated GGT levels,*** effects were observed for the interaction between BD and abdominal ***:Obesity aggravated the odds of elevated liver AST and GGT levels in HIBD.

关键词： obesity binge drinking liver enzymes logistic regression additive effects

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A study of CBIR(Content-based Image Retrieval) computer-aided diagnosis system of breast ultrasound images using similarity measures of distance

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Transactions of the Korean Institute of Electrical Engineers 2017年第8期66卷 1272-1277页

作者： Kim, Min-Jeong Cho, Hyun-Chong Division of Electrical and Electronic Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

To assist radiologists for the characterization of breast masses, Computer-aided Diagnosis(CADx) system has been studied. The CADx system can improve the diagnostic accuracy of radiologists by providing objective information about breast masses. Morphological and texture features were extracted from the breast ultrasound images. Based on extracted features, the CADx system retrieves masses that are similar to a query mass from a reference library using a k-nearest neighbor (k-NN) approach. Eight similarity measures of distance, Euclidean, Chebyshev(Minkowski family), Canberra, Lorentzian(F2 family), Wave Hedges, Motyka(Intersection family), and Cosine, Dice(Inner Product family) are evaluated by ROC(Receiver Operating Characteristic) analysis. The Inner Product family measure used with the k-NN classifier provided slightly higher performance for classification of malignant and benign masses than those with the Minkowski, F2 , and Intersection family measures. Copyright © The Korean Institute of Electrical Engineers.

关键词： Computer aided diagnosis

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Facial age estimation using convolutional neural networks based on inception modules

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Transactions of the Korean Institute of Electrical Engineers 2018年第9期67卷 1224-1231页

作者： Sukh-Erdene, Bolortuya Cho, Hyun-Chong Dept. of Electronic Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%. Copyright © The Korean Institute of Electrical Engineers.

关键词： Face recognition

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A comparison of active contour algorithms in computer-aided detection system for dental cavity using x-ray image

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Transactions of the Korean Institute of Electrical Engineers 2018年第12期67卷 1678-1684页

作者： Kim, Dae-han Heo, Chang-hoe Cho, Hyun-chong Dept. of Electronic Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Department of Medicine University of Ulsan College of Medicine Korea Republic of

- Dental caries is one of the most popular oral disease. The aim of automatic dental cavity detection system is helping dentist to make accurate diagnosis. It is very important to separate cavity from the teeth in the detection system. In this paper, We compared two active contour algorithms, Snake and DRLSE(Distance Regularized Level Set Evolution). To improve performance, image is selected ROI(region of interest), then applied bilateral filter, Canny edge. In order to evaluate the algorithms, we applied to 7 tooth phantoms from incisor to molar. Each teeth contains two cavities of different shape. As a result, Snake is faster than DRLSE, but Snake has limitation to compute topology of objects. DRLSE is slower but those of performance is better. Copyright © The Korean Institute of Electrical Engineers.

关键词： Image segmentation

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