Isbn: 9786203202656 - x-ray images classifications using optimized deep learning (5 resultados)

Taschenbuch. Condición: Neu. X-ray Images Classifications Using Optimized Deep Learning | Mahesh Jangid (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203202656 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deep Convolutional Neural Networks or simply Convolutional Neural Networks (CNN) have recently become one of the most powerful and expressive learning models for Image Pattern Recognition, Medical Image Processing, Computer Vision, Handwritten/ Optical Character Recognition, etc. that are well-versed in performing the Classification tasks, both Binary as well as Categorical in an efficient and simple manner. Besides its wide use in various fields and domains these days, it has gained high popularity and recognition in the area of Medical Science as various Medical reports these days are highly reliable on the Deep Learning based Image recognition. In this book, we trained a Deep Structured Neural Network Model, which is basically a CNN Model over a large set of X-RAY Images Dataset called MURA (Musculoskeletal Radiographs Abnormality) and tried to predict the Abnormalities of a Radiographic Image (whether an Image is Normal or Abnormal) based on Binary classifications. 80 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deep Convolutional Neural Networks or simply Convolutional Neural Networks (CNN) have recently become one of the most powerful and expressive learning models for Image Pattern Recognition, Medical Image Processing, Computer Vision, Handwritten/ Optical Character Recognition, etc. that are well-versed in performing the Classification tasks, both Binary as well as Categorical in an efficient and simple manner. Besides its wide use in various fields and domains these days, it has gained high popularity and recognition in the area of Medical Science as various Medical reports these days are highly reliable on the Deep Learning based Image recognition. In this book, we trained a Deep Structured Neural Network Model, which is basically a CNN Model over a large set of X-RAY Images Dataset called MURA (Musculoskeletal Radiographs Abnormality) and tried to predict the Abnormalities of a Radiographic Image (whether an Image is Normal or Abnormal) based on Binary classifications.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jangid MaheshDr. Mahesh Jangid is Associate Professor in Department of Computer Science & Engineering, Manipal University Jaipur, having 11 years of teaching and Research experience with prestigious academic institutions. He has an i.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deep Convolutional Neural Networks or simply Convolutional Neural Networks (CNN) have recently become one of the most powerful and expressive learning models for Image Pattern Recognition, Medical Image Processing, Computer Vision, Handwritten/ Optical Character Recognition, etc. that are well-versed in performing the Classification tasks, both Binary as well as Categorical in an efficient and simple manner. Besides its wide use in various fields and domains these days, it has gained high popularity and recognition in the area of Medical Science as various Medical reports these days are highly reliable on the Deep Learning based Image recognition. In this book, we trained a Deep Structured Neural Network Model, which is basically a CNN Model over a large set of X-RAY Images Dataset called MURA (Musculoskeletal Radiographs Abnormality) and tried to predict the Abnormalities of a Radiographic Image (whether an Image is Normal or Abnormal) based on Binary classifications.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.…