X-ray Images Classifications Using Optimized Deep Learning - Jangid, Mahesh;Panda, Shubhajit;Chaurasia, Sandeep
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
El Dr. Mahesh Jangid es profesor asociado del Departamento de Ciencias e Ingeniería Informática de la Universidad de Manipal, Jaipur, y cuenta con 11 años de experiencia en la enseñanza y la investigación en prestigiosas instituciones académicas. Tiene un historial académico impecable y un gran interés por la investigación. Está cualificado para GATE, SET y NET.