Name: Classification and Localization of eye Diseases Using cnn (en Inglés)
SKU: 63484060
Price: 1052.33 MXN
Availability: InStock
Author: Anupreeta Bidarkote; Somnath Thigale; Tukaram Chavan
ISBN: 9786207998562

portada Classification and Localization of eye Diseases Using cnn (en Inglés)

Classification and Localization of eye Diseases Using cnn (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Anupreeta Bidarkote; Somnath Thigale; Tukaram Chavan

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786207998562

Classification and Localization of eye Diseases Using cnn (en Inglés)

Anupreeta Bidarkote; Somnath Thigale; Tukaram Chavan (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 23 a 28 días háb.

$ 1,753.88$ 1,052.33

-40%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 1,052.33

¡Envío Gratis! Llega entre el 08 Oct y el 16 Oct a CUAUHTÉMOC, Ciudad de México. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Classification and Localization of eye Diseases Using cnn (en Inglés)"

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.