Article
Biology
Yugen Yi, Yan Jiang, Bin Zhou, Ningyi Zhang, Jiangyan Dai, Qinqin Zeng, Wei Zhou
Summary: Glaucoma, a leading cause of blindness and visual impairment globally, requires early screening and diagnosis to prevent vision loss. Deep learning methods have shown promising results in optic disk and optic cup segmentation and have been incorporated into CAD systems. However, the complexity of clinical data poses challenges. In this study, a novel Coarse-to-Fine Transformer Network (C2FTFNet) is proposed to jointly segment the optic disk and optic cup, utilizing U-Net, Circular Hough Transform, TransUnet3+, Transformer module, and Multi-Scale Dense Skip Connection. Experimental results on multiple datasets validate the superior effectiveness of C2FTFNet compared to existing approaches.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Qianlong Zhu, Xinjian Chen, Qingquan Meng, Jiahuan Song, Gaohui Luo, Meng Wang, F. E. Shi, Zhongyue Chen, Dehui Xiang, Lingjiao Pan, Zuoyong LI, Weifang Zhu
Summary: This paper proposes a general OD and OC segmentation network based on deep learning, adopting a mixed training strategy with different datasets and effectively overcoming the problems of domain shift and inadequate training through newly designed modules. Experimental results demonstrate the competitiveness of this network on multiple fundus image datasets.
BIOMEDICAL OPTICS EXPRESS
(2021)
Article
Biology
Nihal Zaaboub, Faten Sandid, Ali Douik, Basel Solaiman
Summary: This study proposes a new and robust method for optic disc segmentation in color retinal fundus images. The method achieves accurate localization and segmentation of the optic disc and demonstrates excellent performance on various databases.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Neurosciences
Yuanyuan Peng, Weifang Zhu, Zhongyue Chen, Fei Shi, Meng Wang, Yi Zhou, Lianyu Wang, Yuhe Shen, Daoman Xiang, Feng Chen, Xinjian Chen
Summary: This article proposes a novel neural network model (AFENet) for the accurate segmentation of optic disc (OD) in fundus images of premature infants. By fusing high-level semantic information and multiscale low-level detailed information from different levels, the model effectively addresses the challenges of complexity, non-uniform illumination, and low contrast between background and target area. Experimental results demonstrate that the proposed model achieves excellent performance in OD segmentation.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
J. H. Gagan, Harshit S. Shirsat, Yogish S. Kamath, Neetha I. R. Kuzhuppilly, J. R. Harish Kumar
Summary: In this paper, we propose a fully automated method for segmenting the optic disc in retinal fundus images using a basis-spline-based active contour. The method achieves segmentation by optimizing the energy of the active contour with respect to five free parameters using gradient descent technique and Green's theorem. The use of these techniques reduces computational cost and speeds up the segmentation task. The method is validated on multiple databases and demonstrates high segmentation accuracy.
Article
Computer Science, Information Systems
Ga Young Kim, Sang Hyeok Lee, Sung Min Kim
Summary: Fundus image is important for diagnosing diseases, and this study proposes an automated method for segmenting the optic disc and fovea, showing high accuracy in localization and segmentation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Biomedical
Akshat Tulsani, Preetham Kumar, Sumaiya Pathan
Summary: This paper presents a novel approach for the identification of glaucoma using a segmentation based approach on the optic disc and optic cup, achieving improved accuracy for image classification and segmentation through tuning hyper parameters and using a custom loss function. The model demonstrates state-of-art results for Intersection over Union (IOU) scores and enhances training time efficiency.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hao Xiong, Sidong Liu, Enrico Coiera, Shlomo Berkovsky, Roneel V. Sharan
Summary: In this study, a weak label based Bayesian U-Net method exploiting Hough transform is proposed for optic disc segmentation in fundus images. By building a probabilistic graphical model and using the state-of-the-art U-Net framework, this method achieves accurate disc segmentation without the need for pixel-level annotation.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Multidisciplinary Sciences
Abdullah Almansour, Mohammed Alawad, Abdulrhman Aljouie, Hessa Almatar, Waseem Qureshi, Balsam Alabdulkader, Norah Alkanhal, Wadood Abdul, Mansour Almufarrej, Shiji Gangadharan, Tariq Aldebasi, Barrak Alsomaie, Ahmed Almazroa
Summary: Glaucoma is the second leading cause of blindness globally, and peripapillary atrophy (PPA) is a morphological symptom associated with it. This study developed a method for detecting PPA using fundus images and deep learning algorithms, which can be used for glaucoma screening. The model achieved good accuracy in ROI localization and PPA classification. Further research is needed to segment PPA boundaries for more detailed detection.
Article
Computer Science, Interdisciplinary Applications
Siqi Wang, Xiaosheng Yu, Wenzhuo Jia, Jianning Chi, Pengfei Lv, Junxiang Wang, Chengdong Wu
Summary: Eye diseases have a considerable impact on human health. This study proposes a weakly-supervised optic disc detection method based on FCN and WLRR to accurately detect the optic disc region in fundus images. The method utilizes low-level features, clustering algorithms, and prior information to accurately segment the optic disc region. Experimental results demonstrate its superior performance compared to existing weakly-supervised methods.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2023)
Article
Computer Science, Software Engineering
Jia-Qi Zhang, Hao-Bin Duan, Jun-Long Chen, Ariel Shamir, Miao Wang
Summary: The task of lane detection in autonomous driving is complex and challenging due to the narrow, fragmented, and often obscured nature of lanes. However, the lanes have a geometric structure resembling a straight line, which can be utilized for improved detection results. To address this challenge, a hierarchical Deep Hough Transform approach is proposed, combining lane features in an image into the Hough parameter space.
COMPUTERS & GRAPHICS-UK
(2023)
Article
Mathematics
Dora Elisa Alvarado-Carrillo, Ivan Cruz-Aceves, Martha Alicia Hernandez-Gonzalez, Luis Miguel Lopez-Montero
Summary: This paper presents a robust method for detecting and piecewise parametric modeling of the Major Temporal Arcade (MTA) in fundus images. Experimental results show that the proposed method outperforms existing approaches in terms of accuracy, pixel distance, and execution time.
Article
Computer Science, Information Systems
Ziyang Chen, Yongsheng Pan, Yong Xia
Summary: Glaucoma affects irreversible blindness, and segmenting the optic disc (OD) and optic cup (OC) on fundus images is key in screening for this disease. However, training a segmentation model that can be deployed across different healthcare centers remains challenging due to variations in image tone, contrast, and brightness. To address this, a novel unsupervised domain adaptation method called RDR-Net is proposed, which includes three modules designed to alleviate the domain gap. Evaluation against other models on four fundus image datasets demonstrates that RDR-Net excels in both performance and generalization ability.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Chemistry, Analytical
Xiaozhong Xue, Linni Wang, Weiwei Du, Yusuke Fujiwara, Yahui Peng
Summary: This paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for accurate optic disc (OD) segmentation in fundus images. The experimental results demonstrate that the proposed method performs well in OD segmentation.
Article
Computer Science, Artificial Intelligence
Mohammad Mahdi Samsami, Seyed Mohammad Salar Zaheryani, Mehran Yazdi
Summary: The paper introduces a method using elliptical Hough transform and correlation-based circular Hough transform to enhance the efficiency and accuracy of iris detection, especially for eye tracking applications. The proposed method can identify closed-eye images and quickly remove them, being both simple and fast.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Dhimas Arief Dharmawan, Boon Poh Ng, Susanto Rahardja
Article
Engineering, Electrical & Electronic
Dhimas Arief Dharmawan, Boon Poh Ng
Proceedings Paper
Engineering, Electrical & Electronic
Yessi Jusman, Muhammad Ahdan Fawwaz Nurkholid, Dhimas Arief Darmawan, Feriandri Utomo
Summary: This study compared the performance of two pretrained models, AlexNet and GoogLeNet, with GoogLeNet showing better accuracy while AlexNet had shorter training and testing times. The research aims to assist researchers in choosing the right architecture for classifying prostate cancer images in terms of time and accuracy.
2021 IEEE REGION 10 SYMPOSIUM (TENSYMP)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Rosyid Prihantoro, Yessi Jusman, Dhimas Arief Dharmawan, Kunnu Purwanto
Summary: The study focused on designing an automated feeder for laying hens using real-time clock (RTC) and Arduino Uno. It effectively reduces the error value in feeding slots and assists breeders in automatic feeding management. The automated feeder system consists of Arduino Uno, RTC for time reading, Stepper motor for feeding collection, and Servo motor for feeding valve actuation.
2021 1ST INTERNATIONAL CONFERENCE ON ELECTRONIC AND ELECTRICAL ENGINEERING AND INTELLIGENT SYSTEM (ICE3IS)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yessi Jusman, Indah Monisa Firdiantika, Dhimas Arief Dharmawan, Kunnu Purwanto
Summary: Skin cancer is a disease caused by abnormal growth of skin cells, commonly occurring on sun-exposed skin. Early detection and classification are highly effective in preventing serious damage from skin cancer. Among the compared networks, the VGG-16 model shows the best classification accuracy in skin cancer classification, while custom CNN and Multi-layer Perceptron models are faster in terms of testing time.
2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Dhimas Arief Dharmawan, Latifah Listyalina
2020 1ST INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, ADVANCED MECHANICAL AND ELECTRICAL ENGINEERING (ICITAMEE 2020)
(2020)
Proceedings Paper
Imaging Science & Photographic Technology
Di Li, Dhimas Arief Dharmawan, Boon Poh Ng, Susanto Rahardja
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2019)
Article
Engineering, Electrical & Electronic
Dhimas Arief Dharmawan, Boon Poh Ng, Narong Borijindargoon
IEEE SIGNAL PROCESSING LETTERS
(2019)
Article
Computer Science, Information Systems
Dhimas Arief Dharmawan, Di Li, Boon Poh Ng, Susanto Rahardja
Proceedings Paper
Biophysics
Dhimas Arief Dharmawan, Boon Poh Ng
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2017)
Article
Engineering, Biomedical
Hanung Adi Nugroho, Dhimas Arief Dharmawan, Litasari, Latifah Listyalina
INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY
(2017)
Proceedings Paper
Computer Science, Information Systems
Hanung Adi Nugroho, Dewi Purnamasari, Indah Soesanti, Widhia K. Z. Oktoeberza, Dhimas Arief Dharmawan
2015 International Conference on Science in Information Technology (ICSITech)
(2015)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hanung Adi Nugroho, Latifah Listyalina, Noor Akhmad Setiawan, Sunu Wibirama, Dhimas Arief Dharmawan
2015 INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL, INFORMATICS AND ITS APPLICATIONS (IC3INA)
(2015)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hanung Adi Nugroho, Dhimas Arief Dharmawan, Indriana Hidayah, Latifah Listyalina
2015 INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL, INFORMATICS AND ITS APPLICATIONS (IC3INA)
(2015)
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
