To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to...To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic algorithms.The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes.In addition,they usually only target one or a few different kinds of eye diseases at the same time.In this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs classification.PLML_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification scores.The DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right CFI.After then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel basis.After the attributes have been analyzed,they are integrated to provide a representation at the patient level.Throughout the whole process of ODs categorization,the patient-level representation will be used.The efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.展开更多
Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented ...Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack depiction.Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping.Indeed,many hospitals lack experienced clinicians to diagnose wrist cracks.Therefore,an automated system is required to reduce the burden on clinicians and identify cracks.In this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the wrist.For the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures.The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s approaches.The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.展开更多
文摘To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic algorithms.The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes.In addition,they usually only target one or a few different kinds of eye diseases at the same time.In this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs classification.PLML_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification scores.The DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right CFI.After then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel basis.After the attributes have been analyzed,they are integrated to provide a representation at the patient level.Throughout the whole process of ODs categorization,the patient-level representation will be used.The efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.
文摘Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack depiction.Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping.Indeed,many hospitals lack experienced clinicians to diagnose wrist cracks.Therefore,an automated system is required to reduce the burden on clinicians and identify cracks.In this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the wrist.For the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures.The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s approaches.The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.
