Deep Convolution Neural Network for Big Data Medical Image Classification

Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classi?cation, image analysis, clinical archives and object recognition. With an extensive utilization of digital images as information in the hospitals, the archives of medical images are growing exponentially. Digital images play a vigorous role in predicting the patient disease intensity and there are vast applications of medical images in diagnosis and investigation.

Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. For classifying the medical images according to their relevant classes a most suitable classi?er is most important. Where we are proposing our model where the algorithm is trained for classifying medical images by deep learning technique. A pre-trained deep convolution neural network (GoogleNet) is used that which can classifies the various medical images for various body organs. This method of image classi?cation is bene?cial to predict the appropriate class or category of unknown images. The results of the experiment exhibit that our method is best suited to classify various medical images. 

Keywords: Medical image classi?cation, pre-trained DCNN, convolution neural network, deep learning