Andere Kunden interessierten sich auch für
![Machine Learning Framework For Automated Detection of Diabetic Lesion]( "Machine Learning Framework For Automated Detection of Diabetic Lesion")
Machine Learning Framework For Automated Detection of Diabetic Lesion23,99 €![Detection & Classification of Skin diseases using Deep Learning]( "Detection & Classification of Skin diseases using Deep Learning")
Detection & Classification of Skin diseases using Deep Learning29,99 €![Automated Retinal Image Segmentation]( "Automated Retinal Image Segmentation")
Automated Retinal Image Segmentation51,99 €![Development of shoe appliance for Diabetic Patient¿s Foot Ulcer in case of Ethiopia]( "Development of shoe appliance for Diabetic Patient¿s Foot Ulcer in case of Ethiopia")
Development of shoe appliance for Diabetic Patient¿s Foot Ulcer in case of Ethiopia26,99 €![Deep Learning with GPUs]( "Deep Learning with GPUs")
Deep Learning with GPUs36,99 €![Dual Shot Face Detecting Using Deep Learning]( "Dual Shot Face Detecting Using Deep Learning")
Dual Shot Face Detecting Using Deep Learning26,99 €![Unveiling the Black Box: Practical Deep Learning and Explainable AI]( "Unveiling the Black Box: Practical Deep Learning and Explainable AI")
Unveiling the Black Box: Practical Deep Learning and Explainable AI51,99 €
In this book, a deep learning technique called Convolution Neural Network (CNN) is deployed for determining the level of DR with accuracy. Hence, for training, this network, fundus image dataset from a public resource called Kaggle is utilized. But, before feeding the dataset into a deep learning model, the images are pre-processed using the libraries present in python, so that the model can accurately classify the test input image into the respective diabetic retinopathy class. Thus, the proposed deep learning model helps in identifying DR automatically at its earlier stage, thus helping in controlling its progression that leads to vision-related problems.