Article
Multidisciplinary Sciences
Summiya Batool, Syed Omer Gilani, Asim Waris, Khawaja Fahad Iqbal, Niaz B. Khan, M. Ijaz Khan, Sayed M. Eldin, Fuad A. Awwad
Summary: Diabetic retinopathy is a major cause of blindness worldwide. Efficient diabetic retinopathy detecting systems are urgently needed for early diagnosis and treatment. This study improves the accuracy and F1 score of the detection models by utilizing deep learning techniques and features extracted from fundus images.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
K. Parthiban, M. Kamarasan
Summary: Diabetic retinopathy is a major cause of preventable blindness for diabetic patients. Regular retinal screening is recommended to detect diabetic retinopathy at an early stage. This study presents an intelligent algorithm based on deep learning for the detection and grading of diabetic retinopathy using retinal fundus images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Biology
Muwei Jian, Hongyu Chen, Chen Tao, Xiaoguang Li, Gaige Wang
Summary: This article proposes a triple-cascade network model (Triple-DRNet) for efficient grading of diabetic retinopathy. The model uses three cascade networks to classify five types of diabetic retinopathy, and achieves improved classification performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Biomedical
Guanghui Yue, Yuan Li, Tianwei Zhou, Xiaoyan Zhou, Yun Liu, Tianfu Wang
Summary: This paper proposes an end-to-end Attention-Driven Cascaded Network (ADCNet) for automatic grading of diabetic retinopathy (DR) from retinal fundus images. It extracts lesion-aware information using a hybrid attention module and an attention-driven aggregation strategy, achieving accurate DR grading.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Ophthalmology
Jian Liu, Yang He, Linghui Kong, Dongni Yang, Nan Lu, Yao Yu, Yuqian Zhao, Yi Wang, Zhenhe Ma
Summary: The association between foveal vessels and retinal thickness in individuals with diabetic retinopathy (DR) and control subjects was investigated in this study. The results showed that the FAZ growth rate was larger in individuals with mild DR compared to control subjects. The findings provide insights into the progression mechanism of DR.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
(2023)
Article
Ophthalmology
Asli Vural, Murat Gunay, Gokhan Celik, Bengi Demirayak, Osman Kizilay
Summary: This study compared foveal microvascular structure, retinal thickness, and BCVA in children with ROP history and healthy children, finding significant foveal microvascular anomalies in ROP children correlated with gestational age and birth weight.
Article
Engineering, Electrical & Electronic
Xiang Liu, Wei Chi
Summary: In this study, a cross-lesion attention network (CLANet) is proposed for automatic diabetic retinopathy (DR) grading. CLANet can adaptively learn complex lesion imaging features and model the dependencies between DR-related lesions. Experimental results show that CLANet outperforms existing methods in DR grading.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Theory & Methods
N. Jagan Mohan, R. Murugan, Tripti Goel, Parthapratim Roy
Summary: Diabetic retinopathy is a complication of diabetes that impairs vision. Early detection and treatment are crucial to prevent vision loss. Manual grading is time-consuming and prone to errors, and protecting patient data privacy is a challenge. Therefore, this study proposes a novel technique based on federated learning to evaluate the severity of diabetic retinopathy while ensuring patient data privacy.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Biology
Peng Cao, Qingshan Hou, Ruoxian Song, Haonan Wang, Osmar Zaiane
Summary: Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In this paper, a unified weakly-supervised domain adaptation framework for DR grading is proposed, which incorporates multi-instance learning and attention mechanisms to model the relationship between patches and images in the target domain. The method achieves high accuracy and shows effectiveness in interpretations, outperforming state-of-the-art approaches.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Ophthalmology
Noriko Terada, Tomoaki Murakami, Kenji Ishihara, Yoko Dodo, Keiichi Nishikawa, Kentaro Kawai, Akitaka Tsujikawa
Summary: The study investigated the clinical significance of intercapillary spaces on swept source optical coherence tomography angiography images in diabetic retinopathy. The quantitative parameters of the spaces were correlated with visual impairment and could potentially serve as an objective diagnostic criterion for diabetic macular ischemia.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
(2022)
Review
Computer Science, Artificial Intelligence
Wadha Almattar, Hamzah Luqman, Fakhri Alam Khan
Summary: Diabetic retinopathy is a major cause of blindness among individuals with diabetes worldwide. Early diagnosis plays a crucial role in preserving vision and stopping the disease from progressing to advanced stages. Machine and deep learning techniques have been effective in diagnosing medical images related to diabetic retinopathy. However, current research primarily focuses on early stage diagnosis, with limited understanding of advanced stage lesions.
IMAGE AND VISION COMPUTING
(2023)
Article
Medicine, General & Internal
Dingying Liao, Zixia Zhou, Fei Wang, Bin Zhang, Yanfen Wang, Yuping Zheng, Jinying Li
Summary: In patients with retinal vein occlusion (RVO), the foveal avascular area (FAZ) enlarges and retinal vein diameter reduces after three consecutive monthly intravitreal ranibizumab injections, with gradual recovery to near baseline levels after 12 months. Ranibizumab therapy may worsen macular ischemia and prevent visual gain in the short term.
FRONTIERS IN MEDICINE
(2023)
Article
Optics
Jian Liu, Shixin Yan, Nan Lu, Dongni Yang, Chunhui Fan, Hongyu Lv, Shuanglian Wang, Xin Zhu, Yuqian Zhao, Yi Wang, Zhenhe Ma, Yao Yu
Summary: This study presents an adaptive watershed algorithm for the automatic extraction of the foveal avascular zone (FAZ) from retinal optical coherence tomography angiography (OCTA) images. The algorithm solves the common problem of over-segmentation in traditional watershed algorithms. Evaluation results show high correlation coefficients and percentages of accurate segmentation, indicating the effectiveness of the algorithm for clinical diagnosis and treatment of eye diseases.
JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES
(2022)
Article
Health Care Sciences & Services
Guisela Fernandez-Espinosa, Carlos Ruiz-Tabuenca, Elvira Orduna-Hospital, Isabel Pinilla, Francisco J. Salgado-Remacha
Summary: The aim of this study was to reduce the variability of manual segmentation of the Foveal Avascular Zone (FAZ) in retinal optical coherence tomography angiography (OCTA) images. A new criterion was established based on the comparison of segmentations by different observers, resulting in smaller FAZ areas and slightly reduced acircularity values in all groups.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Ophthalmology
Zhiyuan Gao, Kai Jin, Yan Yan, Xindi Liu, Yan Shi, Yanni Ge, Xiangji Pan, Yifei Lu, Jian Wu, Yao Wang, Juan Ye
Summary: A deep learning system based on fundus fluorescein angiography (FFA) images was developed and validated for grading diabetic retinopathy (DR). The system achieved high accuracy and AUC values on both internal and external datasets. It has the potential to assist clinical practitioners in diagnosing and treating DR patients, and lays a foundation for future applications in ophthalmic and general diseases.
GRAEFES ARCHIVE FOR CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Noor Akhmad Setiawan, Hanung Adi Nugroho, Anugerah Galang Persada, Tito Yuwono, Ipin Prasojo, Ridho Rahmadi, Adi Wijaya
Summary: This study proposed a method based on a cascaded transparent classifier for handling the classification of ECG signals related to arrhythmia. By combining feature extraction and cascaded classifier, automatic detection and classification of arrhythmia were achieved with high accuracy and rule generation.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Engineering, Biomedical
Inung Wijayanto, Rudy Hartanto, Hanung Adi Nugroho
Summary: This study proposed a new feature extraction method based on FDispEn for evaluating the complexity of biological signals. By expanding the measurement distance of adjacent elements using the multi-distance signal level differences (MSLD) method, the multi-distance FDispEn (MFDispEn) method was introduced. Compared to other signal complexity evaluation methods, MFDispEn and MFDF show better separability in handling epileptic EEG signals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Information Systems
R. Nurfauzi, H. A. Nugroho, I. Ardiyanto, E. L. Frannita
Summary: Male lung cancer has the highest mortality rate, with juxta-pleural and juxtavascular nodules being the most common types on the lung surface. The research aims for fast computational time and low error in covering nodule areas.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Dermatology
Erwin Setyo Nugroho, Igi Ardiyanto, Hanung Adi Nugroho
SKIN RESEARCH AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Muhammad Ilhamdi Rusydi, Mir'atul Khairiah, Kalputra Hadi, Agung Wahyu Setiawan, Ises Reni, Hermawan Nugroho, Noverika Windasari
Summary: This study developed a novel control method for operating an electric wheelchair using hand gestures, specifically wrist rotation. The study involved 65 participants and investigated two gesture classifying methods, with the Naive Bayes approach showing the most promising results. Evaluations with six participants demonstrated that the developed wheelchair system could be controlled comfortably and accurately, addressing finger dependencies and hand fatigue.
Proceedings Paper
Computer Science, Information Systems
Fahmizal, Hanung Adi Nugroho, Adha Imam Cahyadi, Igi Ardiyanto
Summary: This study presents a linear quadratic regulator (LQR) for controlling a twin rotor MIMO system (TRMS) with Simechanics simulation. The results show improved system stability and faster response time by adding Nbar gain to reduce steady-state error compared to PID control. The effectiveness of the proposed approach is demonstrated using Simechanics in the Matlab environment.
2021 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND INFORMATION ENGINEERING (ICEEIE 2021)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Hanung Adi Nugroho, Eka Legya Frannita, Rizki Nurfauzi
Summary: Researchers proposed a new scheme for detecting and segmenting thyroid nodules, which achieved over 90% success rate in testing. The proposed scheme has the potential to be integrated as part of an intelligent system for detecting and segmenting thyroid cancer.
2021 8TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTERSCIENCE AND INFORMATICS (EECSI) 2021
(2021)
Proceedings Paper
Engineering, Biomedical
Hanung Adi Nugroho, Rizki Nurfauzi
Summary: Malaria is a disease transmitted by a female mosquito anopheles bite, commonly occurring in tropical and sub-tropical regions. Early diagnosis of malaria has been proven to effectively prevent malaria-related mortality, and automated malaria detection studies have shown promising performance in reducing manual microscopy-based examination times.
2021 IEEE INTERNATIONAL BIOMEDICAL INSTRUMENTATION AND TECHNOLOGY CONFERENCE (IBITEC): THE IMPROVEMENT OF HEALTHCARE TECHNOLOGY TO ACHIEVE UNIVERSAL HEALTH COVERAGE
(2021)
Review
Engineering, Multidisciplinary
Amin Siddiq Sumi, Hanung Adi Nugroho, Rudy Hartanto
Summary: This study conducted a systematic literature review to identify the current research progress in the area of Plasmodium parasite detection. The research found that machine learning algorithms have been widely applied with performance achievements ranging from 60% to 95% in detecting the parasites. The development of artificial intelligence, specifically in machine and deep learning, is considered the most effective approach for Plasmodium parasite detection.
INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY INNOVATION
(2021)
Proceedings Paper
Computer Science, Information Systems
Fathania Firwan Firdaus, Hanung Adi Nugroho, Indah Soesanti
Summary: This research utilizes a deep neural network for detecting heart disease and improves diagnostic accuracy through hyperparameter tuning. Random search spends less time than Bayesian optimization and grid search for tuning. Bayesian optimization yields higher accuracy compared to grid search and random search in terms of classification performance results.
2021 IEEE ASIA PACIFIC CONFERENCE ON WIRELESS AND MOBILE (APWIMOB)
(2021)
Article
Management
Anan Nugroho, Risanuri Hidayat, Hanung Adi Nugroho, Johan Debayle
Summary: The study presents an automated system called MoRbAC for detecting suspicious ultrasound objects, achieving an average accuracy of up to 98.58% and short mean execution time when applied to breast lesions and thyroid nodules. The results demonstrate the effectiveness and efficiency of MoRbAC as an empowered method for computer-aided diagnosis (CAD).
INTERNATIONAL JOURNAL OF INNOVATION AND LEARNING
(2021)
Article
Computer Science, Information Systems
Hanung Adi Nugroho, Zulfanahri, Eka Legya Frannita, Igi Ardiyanto, Lina Choridah
Summary: A computer-aided diagnosis system for thyroid cancer has been developed, which analyzes internal and external characteristics to classify nodules, showing that the system is reliable in assisting radiologists with classification.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Swaraj Dube, Wong Yee Wan, Hermawan Nugroho
Summary: With the exponential growth of IoT devices, the challenge lies in the transmission cost of data in the edge-cloud architecture. Our proposed methods effectively perform class-incremental learning.
Proceedings Paper
Automation & Control Systems
Chew Min Kang, Loh Chow Yeh, Sam Yap Ren Jie, Tan Jing Pei, Hermawan Nugroho
INTELLIGENT ROBOTICS AND APPLICATIONS
(2020)
Proceedings Paper
Computer Science, Information Systems
Hanung Adi Nugroho, Eka Legya Frannita, Augustine Henni Tita Hutami, Lina Chondah, Anan Nugroho, Rizki Nur Fauzi, Nurhuda Hendra Setiawan
ICACSIS 2020: 2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS)
(2020)
