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Jae-Hyuk Cha

Hanyang University · Computer Science

About the Lab

Professor Jae-Hyuk Cha's research lab specializes in advancing artificial intelligence and deep learning for medical image analysis, with a strong focus on early disease detection and diagnosis across various clinical domains. The lab develops innovative computer vision techniques, particularly deep convolutional neural networks (DCNNs), combined with explainable AI and data augmentation to improve accuracy in detecting skin cancer, lung abnormalities, diabetic retinopathy, and leukemia. A key emphasis is placed on overcoming challenges such as class imbalance, low-contrast lesions, and high-dimensional data through intelligent feature selection and robust model optimization. The lab also explores online healthcare prediction systems to enhance accessibility and reliability in digital health platforms.

medical image analysisdeep learningexplainable AIdisease detectioncomputer vision in healthcare

Research Overview

Papers
166
Total Citations
1,874
Papers (5y)
56
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
56total
2022
2023
2024
2025
2026
Citations per year (5y)
994total
20222023202420252026

Selected Papers

15
1
Article|105 citations·2005
Extended Hough transform for linear feature detection
Jaehyuk Cha, Rufus H. Cofer, Samuel P. Kozaitis
SJR Q1Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science
2
Article|60 citations·2023
A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI
Naveed Ahmad, Jamal Hussain Shah, Muhammad Attique Khan, Jamel Baili, Ghulam Jillani Ansari, Usman Tariq, Ye Jin Kim, Jaehyuk Cha
SJR Q2Frontiers in OncologyOA

Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely diffi

OncologyMedicine
3
Article|56 citations·2023
CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model
Marriam Nawaz, Tahira Nazir, Jamel Baili, Muhammad Attique Khan, Ye Jin Kim, Jaehyuk Cha
SJR Q2DiagnosticsOA

The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduce

Radiology, Nuclear Medicine and ImagingMedicine
4
Article|48 citations·2022
COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization
Ameer Hamza, Muhammad Attique Khan, Shuihua Wang, Majed Alhaisoni, Meshal Alharbi, Hany S. Hussein, Hammam Alshazly, Ye Jin Kim, Jaehyuk Cha
SJR Q1Frontiers in Public HealthOA

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized tech

Radiology, Nuclear Medicine and ImagingMedicine
5
Article|26 citations·2023
Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks
Swaminathan Sundaram, Meganathan Selvamani, Sekar Kidambi Raju, Seethalakshmi Ramaswamy, Saiful Islam‎, Jaehyuk Cha, Nouf Abdullah Almujally, Ahmed Elaraby
SJR Q2DiagnosticsOA

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term dia

Radiology, Nuclear Medicine and ImagingMedicine
6
Article|24 citations·2023
Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection
Riaz Ahmad, Muhammad Awais, Nabeela Kausar, Usman Tariq, Jaehyuk Cha, Jamel Balili
SJR Q1CancersOA

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very

Computer Vision and Pattern RecognitionComputer Science
7
Article|15 citations·2022
Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner
E. Rajesh, Shajahan Basheer, Rajesh Kumar Dhanaraj, Soni Yadav, Seifedine Kadry, Muhammad Attique Khan, Ye Jin Kim, Jaehyuk Cha
SJR Q2DiagnosticsOA

The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always r

Radiology, Nuclear Medicine and ImagingMedicine
8
Article|14 citations·2023
Empowering and conquering infirmity of visually impaired using AI‐technology equipped with object detection and real‐time voice feedback system in healthcare application
Hania Tarik, Shahzad Hassan, Rizwan Ali Naqvi, Saddaf Rubab, Usman Tariq, Monia Hamdi, Hela Elmannai, Ye Jin Kim, Jaehyuk Cha
SJR Q1CAAI Transactions on Intelligence TechnologyOA

Abstract The Internet of Things is emerging as a crucial technology in aiding humans and making their lives easier. Among the human population, a large percentage of people suffer from disabilities resulting in challenges in everyday life particularly people with visual disabilities. While several inventions exist to aid people with blindness in their everyday lives, the tools are not adequate in terms of accessibility and efficiency. Smart cane is a useful technology that is being researched an

Cognitive NeuroscienceNeuroscience
9
Article|11 citations·2023
A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton–Raphson-Based Feature Selection Approach for Human Gait Recognition
Faiza Jahangir, Muhammad Attique Khan, Majed Alhaisoni, Abdullah Alqahtani, Shtwai Alsubai, Mohemmed Sha, Abdullah Al Hejaili, Jaehyuk Cha
SJR Q1SensorsOA

The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging

Biomedical EngineeringEngineering
10
Article|5 citations·2021
The Effect of UI Usability of Mobile Healthcare Applications on Technostress and Continuous Use Intention: Focusing on Elderly Users
Jaehyuk Cha
Journal of Digital ConvergenceOA
Social PsychologyPsychology
11
Book Chapter|4 citations·2007
COMIB: Composite Icon Browser for Multimedia Databases
Jaehyuk Cha, Sukho Lee
Computer Vision and Pattern RecognitionComputer Science
12
Article|3 citations·1996
COMIB: COMposite Icon Browser for multimedia databases
Jaehyuk Cha, Sukho Lee
SJR Q1Multimedia Tools and Applications
Computer Vision and Pattern RecognitionComputer Science
13
Article|3 citations·2018
Effects of SNS community use of outdoor sports participants on social capital and word-of-mouth
Jaehyuk Cha, Kyung-Ro Chang, Won Jae Seo
Korean Journal of Sport Management

본 연구는 아웃도어 스포츠 SNS 커뮤니티 참여자의 SNS 커뮤니티 활동이 사회적 자본 형성과 구전의도에 미치는 영향을 검증하는 데 목적이 있다. 구체적으로 스포츠 SNS 커뮤니티 활동이 아웃도어 스포츠 참여에 대한 긍정적 구전에 미치는 영향을 분석하고 이들 간의 관계에서 사회적 자본의 매개효과를 검증하고자 하였다. 이를 위해 아웃도어 스포츠 관련 SNS 커뮤니티의 참여자를 대상으로 편의표본추출법과 눈덩이 표본추출법을 통해 설문조사를 실시하여 총 278명의 유효 표본을 확보하였다. 수집된 자료를 바탕으로 빈도분석, 신뢰도 분석, 확인적 요인분석, 상관관계분석, 구조방정식 모형분석을 실시하였다. 분석 결과, 아웃도어 스포츠 참여자의 SNS 커뮤니티 활동은 연결적, 결속적 사회 자본 형성과 구전의도에 정(+)적인 영향을 미치는 것으로 나타났으며, 사회적 자본 중 연결적 사회 자본만이 구전의도에 정(+)적인 영향을 미치는 것으로 나타났다. 또한, SNS 이용과 긍정적 구전의도 간의 관계에

Public Health, Environmental and Occupational HealthMedicine
14
Article|3 citations·2023
A WebExtension framework for experimentation and evaluation of webpage segmentation methods
Geunseong Jung, Jaehyuk Cha
SJR Q3SoftwareXOA

Current webpages contain areas with different functions and contents. Many studies and applications have used webpage segmentation methods to separate these areas or extract only specific areas for their purposes. Examining these methods requires laborious tasks, such as collecting many webpages, inspecting them with human participants, and applying various performance metrics to their results. Therefore, we developed a WebExtension (browser extension) framework to support the examination and an

Information SystemsComputer Science
15
Article|2 citations·2023
A Study of the Continuous Use Intention of Interactive Exercise Games : Application of the Value-Based Acceptance Model
Jaehyuk Cha, Tian Chen, Kyung-Ro Chang
Korean Journal of Sport Management

이 연구는 가치기반수용모델(VAM)을 적용하여 체감형 운동 게임의 지속적 사용에 미치는 영향요인 간의 구조적 관계를 규명하는데 목적이 있다. 이를 위해 콘솔 기반의 체감형 운동 게임을 경험해본 사용자를 모집단으로 설정하여 2023년 3월 한 달에 걸쳐 설문조사를 시행하였다. 자료 수집은 COVID-19 감염병 예방을 위해 비대면으로 진행하였으며, 게임 관련 SNS와 온라인 커뮤니티를 대상으로 이루어졌다. 총 262명의 자료가 분석에 활용되었으며, 빈도 분석, 신뢰도 분석, 확인적 요인분석, 상관관계 분석, 구조방정식 모형분석을 활용하였다. 분석 결과, 첫째, 지각된 편익의 두 요소인 유용성과 유희성은 지각된 가치에 정(+)의 영향을 미치는 것으로 나타났다. 둘째, 지각된 희생의 두 요소 중 비용은 지각된 가치에 부(-)의 영향을 미치는 것으로 나타났으나 복잡성은 유의미한 영향 관계가 나타나지 않았다. 셋째, 지각된 가치는 지속 사용의도에 정(+)의 영향을 미치는 것으로 나타났다. 연

Public Health, Environmental and Occupational HealthMedicine

Research Areas

Computer Networks and CommunicationsInformation SystemsPublic Health, Environmental and Occupational HealthArtificial IntelligenceSociology and Political ScienceRadiology, Nuclear Medicine and Imaging

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