Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma...Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait time.For automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio.Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features.Localizing the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease detection.The approach presented in this study is based on deep classifiers for OD segmentation and glaucoma detection.First,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy metrics.The OD region is then fed into the second stage for glaucoma detection.Therefore,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc area.For this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and fine-tuned.We also used the Support Vector Machine Classifier.The feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor Filters.The final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is provided.Classification metrics including accuracy and ROC curve are compared for each classification method.The novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global method.Moreover,the fusion-based decision system uses the glaucoma detection result obtained using several convolutional deep neural networks and the support vector machine classifier.These classification methods contribute to producing robust classification results.This method was evaluated using well-known retinal images available for research work and a combined dataset including retinal images with and without pathology.The performance of the models was tested on two public datasets and a combined dataset and was compared to similar research.The research findings show the potential of this methodology in the early detection of glaucoma,which will reduce diagnosis time and increase detection efficiency.The glaucoma assessment achieves about 98%accuracy in the classification rate,which is close to and even higher than that of state-of-the-art methods.The designed detection model may be used in telemedicine,healthcare,and computer-aided diagnosis systems.展开更多
Purpose: To show epidemiological and imaging aspects of congenital optic disc abnormalities diagnosed late. Method: It was a retrospective study, including all patients with congenital optic disc abnormalities diagnos...Purpose: To show epidemiological and imaging aspects of congenital optic disc abnormalities diagnosed late. Method: It was a retrospective study, including all patients with congenital optic disc abnormalities diagnosed at a late age between January 2020 and October 2022 at the eye center of Abass Ndao Hospital. Complete ophthalmological examination was performed with eye imaging according to the cases. Results: 09 patients (10 eyes) were diagnosed with congenital optic disc abnormalities. The mean age was 29 years, with a sex ratio of 0.8. Three patients had consulted for unilateral decreased visual acuity since childhood, two for sudden vision loss and in four cases the diagnosis was fortuitous. Visual acuity was ranged from 1/200 to 20/20. Fundus examination showed myelinated retinal nerve fibers in four eyes, optic disc pit in three eyes including two complicated by maculopathy, two cases of morning glory syndrome and a case of pseudoduplication of the optic disc. Optical coherence tomography, ocular ultrasound B and OCT-Angiography were performed according to the cases. Conclusion: Congenital optic disc abnormalities are often diagnosed late. They are potentially amblyogenic and complications are not rare, worsening the visual prognosis. Their screening should be systematic by ophthalmological examination in newborns.展开更多
The increase in payload capacity of trucks has heightened the demand for cost-effective yet high performance brake discs.In this work,the thermal fatigue and wear of compacted graphite iron brake discs were investigat...The increase in payload capacity of trucks has heightened the demand for cost-effective yet high performance brake discs.In this work,the thermal fatigue and wear of compacted graphite iron brake discs were investigated,aiming to provide an experimental foundation for achieving a balance between their thermal and mechanical properties.Compacted graphite iron brake discs with different tensile strengths,macrohardnesses,specific heat capacities and thermal diffusion coefficients were produced by changing the proportion and strength of ferrite.The peak temperature,pressure load and friction coefficient of compacted graphite iron brake discs were analyzed through inertia friction tests.The morphology of thermal cracks and 3D profiles of the worn surfaces were also discussed.It is found that the thermal fatigue of compacted graphite iron discs is determined by their thermal properties.A compacted graphite iron with the highest specific heat capacity and thermal diffusion coefficient exhibits optimal thermal fatigue resistance.Oxidization of the matrix at low temperatures significantly weakens the function of alloy strengthening in hindering the propagation of thermal cracks.Despite the reduced hardness,increasing the ferrite proportion can mitigate wear loss resulting from low disc temperatures and the absence of abrasive wear.展开更多
基金Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding after Publication,Grant No(43-PRFA-P-31).
文摘Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait time.For automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio.Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features.Localizing the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease detection.The approach presented in this study is based on deep classifiers for OD segmentation and glaucoma detection.First,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy metrics.The OD region is then fed into the second stage for glaucoma detection.Therefore,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc area.For this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and fine-tuned.We also used the Support Vector Machine Classifier.The feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor Filters.The final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is provided.Classification metrics including accuracy and ROC curve are compared for each classification method.The novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global method.Moreover,the fusion-based decision system uses the glaucoma detection result obtained using several convolutional deep neural networks and the support vector machine classifier.These classification methods contribute to producing robust classification results.This method was evaluated using well-known retinal images available for research work and a combined dataset including retinal images with and without pathology.The performance of the models was tested on two public datasets and a combined dataset and was compared to similar research.The research findings show the potential of this methodology in the early detection of glaucoma,which will reduce diagnosis time and increase detection efficiency.The glaucoma assessment achieves about 98%accuracy in the classification rate,which is close to and even higher than that of state-of-the-art methods.The designed detection model may be used in telemedicine,healthcare,and computer-aided diagnosis systems.
文摘Purpose: To show epidemiological and imaging aspects of congenital optic disc abnormalities diagnosed late. Method: It was a retrospective study, including all patients with congenital optic disc abnormalities diagnosed at a late age between January 2020 and October 2022 at the eye center of Abass Ndao Hospital. Complete ophthalmological examination was performed with eye imaging according to the cases. Results: 09 patients (10 eyes) were diagnosed with congenital optic disc abnormalities. The mean age was 29 years, with a sex ratio of 0.8. Three patients had consulted for unilateral decreased visual acuity since childhood, two for sudden vision loss and in four cases the diagnosis was fortuitous. Visual acuity was ranged from 1/200 to 20/20. Fundus examination showed myelinated retinal nerve fibers in four eyes, optic disc pit in three eyes including two complicated by maculopathy, two cases of morning glory syndrome and a case of pseudoduplication of the optic disc. Optical coherence tomography, ocular ultrasound B and OCT-Angiography were performed according to the cases. Conclusion: Congenital optic disc abnormalities are often diagnosed late. They are potentially amblyogenic and complications are not rare, worsening the visual prognosis. Their screening should be systematic by ophthalmological examination in newborns.
基金supported by the Science and Technology Innovation Development Project of Yantai(No.2023ZDX016)。
文摘The increase in payload capacity of trucks has heightened the demand for cost-effective yet high performance brake discs.In this work,the thermal fatigue and wear of compacted graphite iron brake discs were investigated,aiming to provide an experimental foundation for achieving a balance between their thermal and mechanical properties.Compacted graphite iron brake discs with different tensile strengths,macrohardnesses,specific heat capacities and thermal diffusion coefficients were produced by changing the proportion and strength of ferrite.The peak temperature,pressure load and friction coefficient of compacted graphite iron brake discs were analyzed through inertia friction tests.The morphology of thermal cracks and 3D profiles of the worn surfaces were also discussed.It is found that the thermal fatigue of compacted graphite iron discs is determined by their thermal properties.A compacted graphite iron with the highest specific heat capacity and thermal diffusion coefficient exhibits optimal thermal fatigue resistance.Oxidization of the matrix at low temperatures significantly weakens the function of alloy strengthening in hindering the propagation of thermal cracks.Despite the reduced hardness,increasing the ferrite proportion can mitigate wear loss resulting from low disc temperatures and the absence of abrasive wear.
