Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address...Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regres-sive Segmentation based Radial Basis Image Classifier(MPCNKFTRS-RBIC)Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time.In MPCNKFTRS-RBIC Model,the ret-inal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuanfilter.Then,preprocessed retinal fundus is given for hidden layer 2 for extracting the features like color,intensity,texture with higher accuracy.After extracting these features,the Tobit Regressive Segmenta-tion process is performed by hidden layer 3 for partitioning preprocessed image within more segments by analyzing the pixel with the extracted features of the fundus image.Then,the segmented image was given to output layer.The radial basis function analyzes the testing image region of a particular class as well as training image region with higher accuracy and minimum time consumption.Simulation is performed with retinal fundus image dataset with various perfor-mance metrics namely peak signal-to-noise ratio,accuracy and time,error rate concerning several retina fundus image and image size.展开更多
Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people...Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.展开更多
The prevalence of melanoma skin cancer has increased in recent decades.The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins.Thus,the early diagno...The prevalence of melanoma skin cancer has increased in recent decades.The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins.Thus,the early diagnosis of melanoma is a key factor in improving the prognosis of the disease.Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images.Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases.This paper proposes a new method which can be used for both skin lesion segmentation and classification problems.This solution makes use of Convolutional neural networks(CNN)with the architecture two-dimensional(Conv2D)using three phases:feature extraction,classification and detection.The proposed method is mainly designed for skin cancer detection and diagnosis.Using the public dataset International Skin Imaging Collaboration(ISIC),the impact of the proposed segmentation method on the performance of the classification accuracy was investigated.The obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%,sensitivity of 92%and specificity of 96%.Also comparing with the related work using the same dataset,i.e.,ISIC,showed a better performance of the proposed method.展开更多
文摘Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regres-sive Segmentation based Radial Basis Image Classifier(MPCNKFTRS-RBIC)Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time.In MPCNKFTRS-RBIC Model,the ret-inal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuanfilter.Then,preprocessed retinal fundus is given for hidden layer 2 for extracting the features like color,intensity,texture with higher accuracy.After extracting these features,the Tobit Regressive Segmenta-tion process is performed by hidden layer 3 for partitioning preprocessed image within more segments by analyzing the pixel with the extracted features of the fundus image.Then,the segmented image was given to output layer.The radial basis function analyzes the testing image region of a particular class as well as training image region with higher accuracy and minimum time consumption.Simulation is performed with retinal fundus image dataset with various perfor-mance metrics namely peak signal-to-noise ratio,accuracy and time,error rate concerning several retina fundus image and image size.
文摘Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.
基金The authors would like to thank the deanship of scientific research and Re-search Center for engineering and applied sciences,Majmaah University,Saudi Arabia,for their support and encouragementthe authors would like also to express deep thanks to our College(College of Science at Zulfi City,Majmaah University,AL-Majmaah 11952,Saudi Arabia)Project No.31-1439.
文摘The prevalence of melanoma skin cancer has increased in recent decades.The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins.Thus,the early diagnosis of melanoma is a key factor in improving the prognosis of the disease.Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images.Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases.This paper proposes a new method which can be used for both skin lesion segmentation and classification problems.This solution makes use of Convolutional neural networks(CNN)with the architecture two-dimensional(Conv2D)using three phases:feature extraction,classification and detection.The proposed method is mainly designed for skin cancer detection and diagnosis.Using the public dataset International Skin Imaging Collaboration(ISIC),the impact of the proposed segmentation method on the performance of the classification accuracy was investigated.The obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%,sensitivity of 92%and specificity of 96%.Also comparing with the related work using the same dataset,i.e.,ISIC,showed a better performance of the proposed method.
