Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,InceptionV3,and MobileNetV2 accuracy are observed to be 92.59%,83.38%,and 90.16%,respectively.The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices.

Mosaic of the Curved Human Retinal Images Based on the Scale-Invariant Feature Transform

To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photographed by a fundus microscope. The kernel of this new algorithm is the scale-,rotation-and illumination-invariant interest point detector & feature descriptor-Scale-Invariant Feature Transform. When matched interest points according to second-nearest-neighbor strategy,the parameters of the model are estimated using the correct matches of the interest points,extracted by a new inlier identification scheme based on Sampson distance from putative sets. In order to preserve image features,bilinear warping and multi-band blending techniques are used to create panoramic retinal images. Experiments show that the proposed method works well with rejection error in 0.3 pixels,even for those cases where the retinal images without discernable vascular structure in contrast to the state-of-the-art algorithms.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid blindness.Retinal Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye illnesses.Exudates can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic retinopathy.With that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level classifications.The model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst level.The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of classification.Exudate detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable results.Furthermore,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose DR.Furthermore,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

A fast method to detect blood vessel in retinal image

Due to the increasing number ot diabetic patients, the number of people affected by diabetic retinopathy isexpected to increase. Diabetic retinopathy is a complication of diabetes and the most serious frequent eye disease in the world. Large-scale retinal screening for diabetic patients is necessary to prevent visual loss and blindness. The analysis of digital retinal images, obtained by the fundus camera, is viewed as a feasible approach because retinal blood vessels have been shown to change in diameter, branching angles, or tortuosity as a result of diabetic retinopathy. The morphological change can help identify the different stages of diabetic retinopathy. In addition, the acquisition of retinal image is nonintrusive and low cost. Automatic segmentation of the retinal blood vessel is a prerequisite for this analysis.~3 This article presents a method to detect blood vessel based on sobel operators.4 Small and fast computation is the outstanding merit of this method.

A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera

Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combining two mirror-symmetric generative adversarial networks(GANs)for image enhancement.Methods:A total of 1047 retinal images were included.The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment.All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists.The quality classification and quality change of images were compared.In addition,imagedetailed reading results for the number of dubiously pathological fundi were also compared.Results:After GAN enhancement,42.9% of images increased their quality,37.5%remained stable,and 19.6%decreased.After excluding the images at the highest level(level 0)before enhancement,a large number(75.6%)of images showed an increase in quality classification,and only a minority(9.3%)showed a decrease.The GANenhanced method was superior for quality improvement over a luminosity and contrast adjustment method(P<0.001).In terms of image reading results,the consistency rate fluctuated from 86.6%to 95.6%,and for the specific disease subtypes,both discrepancy number and discrepancy rate were less than 15 and 15%,for two ophthalmologists.Conclusions:Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.

Microstructural morphology and visual acuity outcome in eyes with epiretinal membrane before,during,and after membrane peeling in intraoperative optical coherence tomography assisted macular surgery

AIM:To measure the difference of intraoperative central macular thickness(CMT)before,during,and after membrane peeling and investigate the influence of intraoperative macular stretching on postoperative best corrected visual acuity(BCVA)outcome and postoperative CMT development.METHODS:A total of 59 eyes of 59 patients who underwent vitreoretinal surgery for epiretinal membrane was analyzed.Videos with intraoperative optical coherence tomography(OCT)were recorded.Difference of intraoperative CMT before,during,and after peeling was measured.Pre-and postoperatively obtained BCVA and spectral-domain OCT images were analyzed.RESULTS:Mean age of the patients was 70±8.13y(range 46-86y).Mean baseline BCVA was 0.49±0.27 log MAR(range 0.1-1.3).Three and six months postoperatively the mean BCVA was 0.36±0.25(P=0.01 vs baseline)and 0.38±0.35(P=0.08 vs baseline)log MAR respectively.Mean stretch of the macula during surgery was 29%from baseline(range 2%-159%).Intraoperative findings of macular stretching did not correlate with visual acuity outcome within 6mo after surgery(r=-0.06,P=0.72).However,extent of macular stretching during surgery significantly correlated with less reduction of CMT at the fovea centralis(r=-0.43,P<0.01)and 1 mm nasal and temporal from the fovea(r=-0.37,P=0.02 and r=-0.50,P<0.01 respectively)3mo postoperatively.CONCLUSION:The extent of retinal stretching during membrane peeling may predict the development of postoperative central retinal thickness,though there is no correlation with visual acuity development within the first 6mo postoperatively.

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

The association between retinal vasculature changes and stroke:a literature review and Meta-analysis

AIM： To determine the association between retinal vasculature changes and stroke.METHODS： MEDLINE and EMBASE were searched for relevant human studies to September 2015 that investigated the association between retinal vasculature changes and the prevalence or incidence of stroke; the studies were independently examined for their qualities. Data on clinical characteristics and calculated summary odds ratios （ORs） were extracted for associations between retinal microvascular abnormalities and stroke, including stroke subtypes where possible, and adjusted for key variables. RESULTS： Nine cases were included in the study comprising 20 659 patients, 1178 of whom were stroke patients. The retinal microvascular morphological markers used were hemorrhage, microaneurysm, vessel caliber, arteriovenous nicking, and fractal dimension. OR of retinal arteriole narrowing and retinal arteriovenous nicking and stroke was 1.42 and 1.91, respectively, indicating that a small-caliber retinal arteriole and retinal arteriovenous nicking were associated with stroke. OR of retinal hemorrhage and retinal microaneurysm and stroke was 3.21 and 3.83, respectively, indicating that retinal microvascular lesions were highly associated with stroke. Results also showed that retinal fractal dimension reduction was associated with stroke （OR： 2.28 for arteriole network, OR： 1.80 for venular network）.CONCLUSION： Retinal vasculature changes have a specific relationship to stroke, which is promising evidence for the prediction of stroke using computerized retinal vessel analysis.

NDC-IVM:An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.

Imaging of human parafoveal area with large field of view in adaptive optics line scanning ophthalmoscope

The parafoveal area,with its high concentration of photoreceptors andfine retinal capillaries,is crucial for central vision and often exhibits early signs of pathological changes.The current adaptive optics scanning laser ophthalmoscope(AOSLO)provides an excellent tool to acquire accurate and detailed information about the parafoveal area with cellular resolution.However,limited by the scanning speed of two-dimensional scanning,thefield of view(FOV)in the AOSLO system was usually less than or equal to 2,and the stitching for the parafoveal area required dozens of images,which was time-consuming and laborious.Unfortunately,almost half of patients are unable to obtain stitched images because of their poorfixation.To solve this problem,we integrate AO technology with the line-scan imaging method to build an adaptive optics line scanning ophthalmoscope(AOLSO)system with a larger FOV.In the AOLSO,afocal spherical mirrors in pairs are nonplanar arranged and the distance and angle between optical elements are optimized to minimize the aberrations,two cylinder lenses are orthogonally placed before the imaging sensor to stretch the point spread function(PSF)for sufficiently digitizing light energy.Captured human retinal images show the whole parafoveal area with 55FOV,60 Hz frame rate and cellular resolutions.Take advantage of the 5FOV of the AOLSO,only 9 frames of the retina are captured with several minutes to stitch a montage image with an FOV of 99,in which photoreceptor counting is performed within approximately 5eccentricity.The AOLSO system not only provides cellular resolution but also has the capability to capture the parafoveal region in a single frame,which offers great potential for noninvasive studying of the parafoveal area.

Analysis of normal human retinal vascular network architecture using multifractal geometry

AIM：To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina.METHODS：Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca,Romania,between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images,corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms,applying the standard boxcounting method. Statistical analyses were performed using the Graph Pad In Stat software.RESULTS：The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα=α_（max）-α_（min））and the spectrum arms＇ heights difference（│Δf│）of the normal images were expressed as mean±standard deviation（SD）：for segmented versions,D_0=1.7014±0.0057; D_1=1.6507±0.0058; D_2=1.5772±0.0059; Δα=0.92441±0.0085; │Δf│= 0.1453±0.0051; for skeletonised versions,D_0=1.6303±0.0051; D_1=1.6012±0.0059; D_2=1.5531± 0.0058; Δα=0.65032±0.0162; │Δf│= 0.0238±0.0161. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα）and the spectrum arms＇ heights difference（│Δf│）of the segmented versions was slightly greater than the skeletonised versions.CONCLUSION：The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.

Three-Dimensional Modeling of the Retinal Vascular Tree via Fractal Interpolation

In recent years,the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed.Several studies describe the various numerical methods to numerical modeling of vascular structures in near-reality.However,the current approaches remain too expensive in terms of storage capacity.Therefore,it is necessary to find the right balance between the relevance of information and storage space.This article adopts two sets of human retinal blood vessel data in 3D to proceed with data reduction in the first part and then via 3D fractal reconstruction,recreate them in a second part.The results show that the reduction rate obtained is between 66%and 95%as a function of the tolerance rate.Depending on the number of iterations used,the 3D blood vessel model is successful at reconstruction with an average error of 0.19 to 5.73 percent between the original picture and the reconstructed image.

Multimodal imaging in acute retinal ischemia: spectral domain OCT, OCT-angiography and fundus autofluorescence

AIM： To describe retinal findings of various imaging modalities in acute retinal ischemia. METHODS： Fluorescein angiography（FA）, spectral domain optical coherence tomography（SD-OCT）, OCTangiography（OCT-A） and fundus autofluorescence（FAF） images of 13 patients（mean age 64y, range 28-86y） with acute retinal ischemia were evaluated. Six suffered from branch arterial occlusion, 2 had a central retinal artery occlusion, 2 had a combined arteriovenous occlusions, 1 patient had a retrobulbar arterial compression by an orbital haemangioma and 2 patients showed an ocular ischemic syndrome.RESULTS： All patients showed increased reflectivity and thickening of the ischemic retinal tissue. In 10 out of 13 patients SD-OCT revealed an additional highly reflective band located within or above the outer plexiform layer. Morphological characteristics were a decreasing intensity with distance from the fovea, partially segmental occurrence and manifestation limited in time. OCT-A showed a loss of flow signal in the superficial and deep capillary plexus at the affected areas. Reduced flow signal was detected underneath the regions with retinal edema. FAF showed areas of altered signal intensity at the posterior pole. The regions of decreased FAF signal corresponded to peri-venous regions. CONCLUSION： Multimodal imaging modalities in retinal ischemia yield characteristic findings and valuable diagnostic information. Conventional OCT identifies hyperreflectivity and thickening and a mid-retinal hyperreflective band is frequently observed. OCT-A examination reveals demarcation of the ischemic retinal area on the vascular level. FAF shows decreased fluorescence signal in areas of retinal edema often corresponding to peri-venous regions.

Retinal axial focusing and multi-layer imaging with a liquid crystal adaptive optics camera

With the help of adaptive optics （AO） technology, cellular level imaging of living human retina can be achieved. Aiming to reduce distressing feelings and to avoid potential drug induced diseases, we attempted to image retina with dilated pupil and froze accommodation without drugs. An optimized liquid crystal adaptive optics camera was adopted for retinal imaging. A novel eye stared system was used for stimulating accommodation and fixating imaging area. Illumination sources and imaging camera kept linkage for focusing and imaging different layers. Four subjects with diverse degree of myopia were imaged. Based on the optical properties of the human eye, the eye stared system reduced the defocus to less than the typical ocular depth of focus. In this way, the illumination light can be projected on certain retina layer precisely. Since that the defocus had been compensated by the eye stared system, the adopted 512 × 512 liquid crystal spatial light modulator （LC-SLM） corrector provided the crucial spatial fidelity to fully compensate high-order aberrations. The Strehl ratio of a subject with -8 diopter myopia was improved to 0.78, which was nearly close to diffraction-limited imaging. By finely adjusting the axial displacement of illumination sources and imaging camera, cone photoreceptors, blood vessels and nerve fiber layer were clearly imaged successfully.

Automated cone photoreceptor cell identication in confocal adaptive optics scanning laser ophthalmoscope images based on object detection

Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophthalmoscope(AOSLO)images.An effectiveness evaluation of identication using the proposed method reveals precision,recall,and F_(1)-score of 95.8%,96.5%,and 96.1%,respectively,considering manual identication as the ground truth.Various object detection and identication results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method.Overall,the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images,being comparable to manual identication.

Determining the imaging plane of a retinal capillary layer in adaptive optical imaging

Even in the early stage,endocrine metabolism disease may lead to micro aneurysms in retinal capillaries whose diameters are less than 10 μm.However,the fundus cameras used in clinic diagnosis can only obtain images of vessels larger than 20 μm in diameter.The human retina is a thin and multiple layer tissue,and the layer of capillaries less than10 μm in diameter only exists in the inner nuclear layer.The layer thickness of capillaries less than 10 μm in diameter is about 40 μm and the distance range to rod＆cone cell surface is tens of micrometers,which varies from person to person.Therefore,determining reasonable capillary layer（CL） position in different human eyes is very difficult.In this paper,we propose a method to determine the position of retinal CL based on the rod＆cone cell layer.The public positions of CL are recognized with 15 subjects from 40 to 59 years old,and the imaging planes of CL are calculated by the effective focal length of the human eye.High resolution retinal capillary imaging results obtained from 17 subjects with a liquid crystal adaptive optics system（LCAOS） validate our method.All of the subjects＇ CLs have public positions from 127 μm to 147 μm from the rod＆cone cell layer,which is influenced by the depth of focus.

Retinal imaging in inherited retinal diseases

Inherited retinal diseases(IRD)are a leading cause of blindness in the working age population.The advances in ocular genetics,retinal imaging and molecular biology,have conspired to create the ideal environment for establishing treatments for IRD,with the first approved gene therapy and the commencement of multiple therapy trials.The scope of this review is to familiarize clinicians and scientists with the current landscape of retinal imaging in IRD.Herein we present in a comprehensive and concise manner the imaging findings of:(I)macular dystrophies(MD)[Stargardt disease(ABCA4),X-linked retinoschisis(RS1),Best disease(BEST1),pattern dystrophy(PRPH2),Sorsby fundus dystrophy(TIMP3),and autosomal dominant drusen(EFEMP1)],(II)cone and cone-rod dystrophies(GUCA1A,PRPH2,ABCA4 and RPGR),(III)cone dysfunction syndromes[achromatopsia(CNGA3,CNGB3,PDE6C,PDE6H,GNAT2,ATF6],blue-cone monochromatism(OPN1LW/OPN1MW array),oligocone trichromacy,bradyopsia(RGS9/R9AP)and Bornholm eye disease(OPN1LW/OPN1MW),(IV)Leber congenital amaurosis(GUCY2D,CEP290,CRB1,RDH12,RPE65,TULP1,AIPL1 and NMNAT1),(V)rod-cone dystrophies[retinitis pigmentosa,enhanced S-Cone syndrome(NR2E3),Bietti crystalline corneoretinal dystrophy(CYP4V2)],(VI)rod dysfunction syndromes(congenital stationary night blindness,fundus albipunctatus(RDH5),Oguchi disease(SAG,GRK1),and(VII)chorioretinal dystrophies[choroideremia(CHM),gyrate atrophy(OAT)].

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient care.Developing a novel ML algorithm specific to Diabetic Retinopathy(DR)is a chal-lenge and need of the hour.Biomedical images include several challenges,including relevant feature selection,class variations,and robust classification.Although the cur-rent research in DR has yielded favourable results,several research issues need to be explored.There is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-fication.This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier(SKPD-PSC)method.The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in har-mony with the Platt Scale Classifier(PSC)to improve the accuracy of DR detection.First,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background noise.Finally,a Platt Scale Classifier(PSC)is applied to the localized features for robust classification.For the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classifiers.Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to the state-of-the-art meth-ods.The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation.Compared to state-of-the-art methods,the method increases DR detection accuracy by 24%and DR detection time by 37.