Andere Kunden interessierten sich auch für
![Dropout Technique for Image Classification]( "Dropout Technique for Image Classification")
Dropout Technique for Image Classification36,99 €![Handwritten Semitic Language Digit Recognition Using Deep Learning]( "Handwritten Semitic Language Digit Recognition Using Deep Learning")
Handwritten Semitic Language Digit Recognition Using Deep Learning39,99 €![Prediction of Concrete Mix Ratio Using Artificial Neural Network]( "Prediction of Concrete Mix Ratio Using Artificial Neural Network")
Prediction of Concrete Mix Ratio Using Artificial Neural Network23,99 €![Ophthalmic Diagnostics Using Eye Tracking Technology]( "Ophthalmic Diagnostics Using Eye Tracking Technology")
Ophthalmic Diagnostics Using Eye Tracking Technology38,99 €![Classification of US liver images using machine learning techniques]( "Classification of US liver images using machine learning techniques")
Classification of US liver images using machine learning techniques46,99 €![A Study on Speaker Verification]( "A Study on Speaker Verification")
A Study on Speaker Verification32,99 €![Classification & Suitability Evaluation of some wetland soils of Ideato, Nigeria]( "Classification & Suitability Evaluation of some wetland soils of Ideato, Nigeria")
Classification & Suitability Evaluation of some wetland soils of Ideato, Nigeria34,99 €
The most common causes of vision loss in people worldwide are cataract, glaucoma, and retinal disorders. The rising prevalence of these diseases necessitates an immediate, accurate diagnosis. The suggested approach is created and designed to make it simple for individuals to diagnose illnesses of the retina, glaucoma, cataract and many more. Artificial neural networks and convolutional neural networks are used to classify and locate eye problems. The suggested approach will reduce the amount of brought-on blindness by enabling patients to receive the necessary care for the mentioned illnesses at an early stage. The chosen method also evaluates the effectiveness and safety of cataract surgery in eyes with age-related macular degeneration in addition to identifying glaucoma and retinal diseases. This study uses photos of the fundus from healthy eyes as well as eyes with glaucoma, cataracts, and retina to show the accuracy of algorithms. Nowadays, the concept of categorizing photographs based on their fundus and extracting features is well recognized, and it also plays a crucial role in the conclusion.