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Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model [article]

C.-H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, I-Hung Lin, Yi-Chieh Liu, Hao-Hsiang Yang, Jesper Tegner
2018 arXiv   pre-print
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.  ...  Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine  ...  We notice that the features learned by deep learning models agree with our intuitions about developing the two-stream machine learning model.  ... 
arXiv:1808.05754v4 fatcat:2uoj4frgezf4jlakgmgpshfdmq

A Tour of Unsupervised Deep Learning for Medical Image Analysis [article]

Khalid Raza, Nripendra Kumar Singh
2018 arXiv   pre-print
Interpretation of medical images for diagnosis and treatment of complex disease from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare.  ...  In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis.  ...  Conflict of Interest Statement Authors declare that there is no any conflict of interest in the publication of this manuscript.  ... 
arXiv:1812.07715v1 fatcat:4dd75wfhvnf7db3v72575tikoi

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Appendix A: Literature selection Pubmed was searched for papers containing "convolutional" OR "deep learning" in any field.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Application of Deep Learning in Medical Image Processing - A Comprehensive Review

Aruna G
2020 IJARCCE  
Deep learning could be a machine learning technique that teaches computers to try to to what comes naturally to humans. the essential concepts and models in deep learning have consequent from the synthetic  ...  The good thing about machine learning in an exceedingly period of medical big data is that considerable hierarchal dealings within the information may be discovered algorithmically exclusive of laborious  ...  disease classification (Plis et al., 2014) 8 SAE MCI/HC classification of fMRI data; Stacked auto-encoders for feature extraction, HMM as a generative model on top (Suk et al.,2016) Table 2 : 2 Deep  ... 
doi:10.17148/ijarcce.2020.9622 fatcat:iw5veqh265fepnvcvpokurux7e

Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques – A Review

Syed Ale Hassan, Shahzad Akbar, Amjad Rehman, Tanzila Saba, Hoshang Kolivand, Saeed Ali Bahaj
2021 IEEE Access  
CONFLICT OF INTEREST The authors of this review paper have no conflict of interest.  ...  Figure 9 shows the methodology of Machine Learning (ML) model in which fundus retinal image is used for classification of CSR.  ...  In this way, the model is first trained using a large dataset of images, and then tested using more data.  ... 
doi:10.1109/access.2021.3108395 fatcat:rt7efw3orjbmpmzmkfvazfxshu

Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques A Review [article]

Syed Ale Hassan, Shahzad Akbar, Amjad Rehman, Tanzila Saba, Hoshang Kolivand, Saeed Ali Bahaj
2021 arXiv   pre-print
In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning  ...  Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more  ...  Figure 9 shows the methodology of Machine Learning (ML) model in which fundus retinal image is used for classification of CSR.  ... 
arXiv:2012.10961v4 fatcat:767inj3zmrgkbigzsh7dwj4bqi

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis – A Review [article]

Sourya Sengupta, Amitojdeep Singh, Henry A.Leopold, Tanmay Gulati, Vasudevan Lakshminarayanan
2019 arXiv   pre-print
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented.  ...  Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.  ...  The DNN performed unsupervised learning of vessel dictionaries using sparse trained denoising auto-encoders (DAE). It was followed by supervised learning of random forest on the DNN response.  ... 
arXiv:1812.07101v3 fatcat:weoh4wnw4ngy5mmq7vwgr2p77e

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
This success started in 2012 when an ML model accomplished a remarkable triumph in the ImageNet Classification, the world's most famous competition for computer vision.  ...  A quick review of current developments with relevant problems in the field of DL used for medical imaging has been provided.  ...  ML uses statistical tools to classify data into two or more groups by learning from the data. Two types of DL models are supervised and unsupervised.  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging.  ...  This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community.  ...  This phenomenon is fundamental to any Machine Learning technique. A complex model inferred using a limited amount of data normally over-fits to the used data and performs poorly on any other data.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Trends in Deep Learning for Medical Hyperspectral Image Analysis

Uzair Khan, Sidike Paheding, Colin Elkin, Vijay Devabhaktuni
2021 IEEE Access  
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  ...  CNN using kernel fusion implemented for cell classification [51] Implementation of CNN for blood cell classification [52] Two-channel CNN for solving limited-samples problem for CNN models [53]  ...  Boltzmann Machines (RBMs) are a relatively 52 simpler deep learning system comprising of two layers: input Table 1 1 78 details the titles of the papers and the category of DL 79 to which the paper  ... 
doi:10.1109/access.2021.3068392 fatcat:mxse6n6f7bbbrognlnbzponr7u

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging.  ...  This enables us to single out "lack of appropriately annotated large-scale data sets" as the core challenge (among other challenges) in this research direction.  ...  This phenomenon is fundamental to any Machine Learning technique. A complex model inferred using a limited amount of data normally over-fits to the used data and performs poorly on any other data.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

A Review on Glaucoma Disease Detection using Computerized Techniques

Faizan Abdullah, Rakhshanda Imtiaz, Hussain Ahmad Madni, Haroon Ahmed Khan, Tariq M. Khan, Mohammad A.U. Khan, Syed S. Naqvi
2021 IEEE Access  
These techniques measure OC and OC dimensions using machine learning based classification and segmentation algorithms.  ...  This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images.  ...  The sequential steps of machine learning based techniques include acquiring of the retinal fundus image from the retinal datasets.  ... 
doi:10.1109/access.2021.3061451 fatcat:lrg4fj4ixje2lilc3k7a33yl2u

Retinal Vessel Segmentation Using Deep Learning: A Review

Chunhui Chen, Joon Huang Chuah, Ali Raza, Yizhou Wang
2021 IEEE Access  
This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning.  ...  The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension.  ...  Supervised models conduct retinal vessel segmentation in two stages: feature extraction and pixels classification.  ... 
doi:10.1109/access.2021.3102176 fatcat:x3jqvx67qndgnoi7wi357b6zwe

Deep learning for photoacoustic imaging: a survey [article]

Changchun Yang, Hengrong Lan, Feng Gao, Fei Gao
2020 arXiv   pre-print
Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models.  ...  Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years.  ...  machine learning models.  ... 
arXiv:2008.04221v4 fatcat:rjocswwer5brrg7ibrzke7ps6i

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Jasem Almotiri, Khaled Elleithy, Abdelrahman Elleithy
2018 Applied Sciences  
helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD).  ...  The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques.  ...  [84] designed a hybrid framework of deep and ensemble learning, where a Deep Neural Network (DNN) was used for unsupervised learning of vesselness via denoising auto-encoder, utilizing sparse trained  ... 
doi:10.3390/app8020155 fatcat:ohixrrcbwrdj3hcdgnib3ne2o4
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