Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.

Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

Hookworm is an illness caused by an internal sponger called a roundworm.Inferable from deprived cleanliness in the developing nations,hookworm infection is a primary source of concern for both motherly and baby grimness.The current framework for hookworm detection is composed of hybrid convolutional neural networks;explicitly an edge extraction framework alongside a hookworm classification framework is developed.To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework,pooling layers are proposed.The hookworms display different profiles,widths,and bend directions.These challenges make it difficult for customized hookworm detection.In the proposed method,a contourlet change was used with the development of the Hookworm detection.In this study,standard deviation,skewness,entropy,mean,and vitality were used for separating the highlights of the each form.These estimations were found to be accurate.AdaBoost classifier was utilized to characterize the hookworm pictures.In this paper,the exactness and the territory under bend examination in identifying the hookworm demonstrate its scientific relevance.