Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks

Abstract

In the concepts of Deep Learning, the ConvNets are a classified group of deep neural networks that are used to analyze the images. They find their major applications in image classification and in the medical image analysis. ConvNets are regularized, multilayer preceptors. The major challenge today is the increasing size of medical image repositories which is leading to troubles in large database management and then querying those databases for storage and retrieval and this can only be sorted out using the Content Based Medical Image Retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low-level visual information captured by the imaging devices and high-level semantic information captured by human. This paper demonstrates a new and efficient framework with the algorithm of Deep CNN for the Feature Based Medical Image Retrieval (FBMIR) system for fast and efficient retrieval of medical and clinical images for detecting Pneumonia. The intermodal datasets are used and divided into two classes to train the network. One class consists of the lungs infected by Pneumonia and the other class contains the images of lungs which are normal. The learned features from the trained deep convolutional neural network with the fast processing feature of the hash method are used to reduce the feature space. To achieve the best retrieval results with class-based predictions 93.9% of average classification accuracy is achieved and 0.87 of mean average precision is achieved for this retrieval task. The proposed framework fit the best for top-ranked multimodal medical image retrieval.