Detection of retinal abnormalities in fu
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Detection of retinal abnormalities in fu
Detection of retinal abnormalities in fu
HALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu ► To cite this version:Mohamed Akil. Yaroub Elloumi, Rostom Kachouri. Detection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks. Elsevier. State of the Art in Neural Networks. 1. Ayman s. El-Baz; Jasjit s. Suri, In press, hal-02428351HAL Id: hal-02428351 https://hal-upec-up Detection of retinal abnormalities in fu em.archives-ouvertes.fr/hal-02428351Submitted on 12 Jan 2020HAL is a multi-disciplinary open access archive for the deposit anil dissemination of scieDetection of retinal abnormalities in fu
ntific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or fHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu niveau recherche, publics OU non. émanant des établissements d’enseignement et de recherche fran^ais OU Ct rangers. des laboratoires publics OU prỉvés.Chapter #.DETECTION OF RETINAL ABNORMALITIESIN FUNDUS IMAGEUSING CNN DEEP LEARNING NETWORKSMohamed Akil“r>. Yaroub Elloumiu,b* , Rostom Kachouri*“Ga Detection of retinal abnormalities in fu spard Monge Computer Science Laboratory. ESIEE-Paris. University Paris-Est Marne-la-Vallée, France.bMedical Technology and Image Processing LaboratoryDetection of retinal abnormalities in fu
. Faculty of medicine. University of Monastic Tunisia.cISITCom Hammam-Sousse. University of Sousse. Tunisia.Corresponding authorName and email: MohameHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu ation (WHO) estimates that 285 million people are visually impaired worldwide, with 39 million blinds. Glaucoma. Cataract. Age-related macular degeneration. Diabetic retinopathy are among the leading retinal diseases. Thus, there is an active effort to create and develop methods to automate screenin Detection of retinal abnormalities in fu g of retinal diseases. Many Computer Aided Diagnosis (CAD) systems for ocular diseases have been developed and are widely used. Deep learning (DL) hasDetection of retinal abnormalities in fu
shown its capabilities in field of public health including ophthalmology. In retinal disease diagnosis, the approach based upon DL and convolutional HALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu esses an overview of the used methods based upon DL and CNNs in detection of retinal abnormalities related to the most severe ocular diseases in retinal images, where network architectures, post/preprocessing and evaluation experiments are detailed. We also present some related work concerning the D Detection of retinal abnormalities in fu eep Learning-based Smartphone applications for earlier screening and diagnosisof retinal diseases.Keywords: Deep Learning. Convolutional Neural NetworDetection of retinal abnormalities in fu
ks. Ocular diseases screening, detection, diagnosis, classification. Smartphone applications1. IntroductionThe WHO estimates that 285 million people aHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu Diabetic retinopathy (DR). Retinitis pigmentosa. Pterygium and Ocular surface neoplasia. There are several causes that contribute to increase the risk of progression and development of these diseases such as family story and genetics, diabetes, obesity, smoking, cardiovascular disease, aging, etc. Detection of retinal abnormalities in fu Therefore, the Dry macular degeneration (Dry AMD) may first develop in one eye and then affect both. The increase of dry AMD stage damages the form ofDetection of retinal abnormalities in fu
the eye. This progression is known as neovascular AMD or wet macular degeneration. For Glaucoma, the openangle glaucoma (OAG) is the most common formHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu DR is the most common type of DR. Glaucoma. AMD. Cataract and DR are the major causes of blindness worldwide [2-5]. However, most ocular diseases affect both eyes and 80% of all causes of visual impairment are preventable or curable [ 11 in early stages. Late stages on ocular pathologies lead always Detection of retinal abnormalities in fu to severe damage on visual acuity and may be irreversible such as the wet AMD. Therefore, early screening, detection and diagnosis of these ocular diDetection of retinal abnormalities in fu
seases are important for slowing down and preventing total vision loss.Nevertheless, early screening is not ensured due to the lack of ophthalmologistHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu f aging patients. Thus, there is an active effort to create and develop methods to automate screening of retinal diseases. Many CAD systems have been expanded and are w idely used for diagnosing ocular diseases [6|.In addition, a variety of imaging modalities been developed to capture the anatomic s Detection of retinal abnormalities in fu tructure of the eye. The principal imaging technologies for the retina, are scanning laser ophthalmoscopy (Scanning laser ophthalmoscope - SLO) 171 anDetection of retinal abnormalities in fu
d optical Coherence Tomography (OCT) |81 and fundus imaging technique [9| which is the commonly used to capture retinal images by fundus camera. RetinHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu ting the anatomic structures in retinal images, such as vessel segmentation 110]. detecting lesions related to DR III], diagnosing glaucoma 112, 13]. AMD ] 14] and cataract 115].The fundus image is direct optical capture of the eye. This image includes (he anatomic structures like Optic Disc (OD). m Detection of retinal abnormalities in fu acula regions, vasculature, blood vessels, lesions such as Red lesions, comprising microaneurysms, hemorrhages, bright lesions, such as exudates, cottDetection of retinal abnormalities in fu
on wool spots, or drusen and vascular abnormalities (see figure 1 and figure 2) by detecting microvasculature changes.Figure /. shows retinal morpholoHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu .Figure 2. Two abnormalities: Hard drusen (left) and Cotton wool spots (right).In assessing ophthalmologic disease pathologies, segmentation of retinal morphology plays a key role and has numerous applications such as OD. Cup. Optic Nene Head (ONH) segmentation, retinal blood vessel segmentation, le Detection of retinal abnormalities in fu sions segmentation and detection based on fundus image. The OCT modality (see an illustration in figure 3) is used for segmenting retinal layer of macDetection of retinal abnormalities in fu
ula and various layers as Inner Nuclear Layer IINL). Outer Nuclear Layer (ONL). Router Plexiform Layer (OPL) and Retinal Nerve Fiber Layer (RNFL). etcHALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri► Detection of retinal abnormalities in fu and OCT structural image showing layers and ONH anatomy, rat central retinal artery (CRA). choroidal microvasculature.HALarchives-ouvertesDetection of Retinal Abnormalities in Fundus Image Using CNN Deep Learning Networks Mohamed Akil, Yaroub Elloumi, Rostom Kachouri►Gọi ngay
Chat zalo
Facebook