Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include...Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine.展开更多
Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of med...Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.展开更多
文摘Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms.Standard diagnostic procedures for DR now include optical coherence tomography and digital fundus imaging.If digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties involved.Optometrists and their patients will find this useful.Using deep convolutional neural networks(DCNNs),we provide a novel approach to this problem.Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones.In order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are trained.To establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine classifier.In our experiments,we used information from Kaggle’s open DR detection database.We fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test it.Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4.Experimental results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be effective.We have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep retina.Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology.This technology might be used for self-diagnosis,at-home care,and telemedicine.
文摘Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.