Diagnosis of Chest Diseases in X-Ray images using Deep Convolutional Neural Network

Learning about Chest Diseases and their characterization is one of the most interesting research topics in recent years. With the various uses of medical images in hospitals, pathologies, and diagnostic centers, the size of the medical image datasets is also expanding expeditiously to capture the diseases in hospitals. Though a lot of researches have been done on this particular topic still this field is confusing and challenging. There are lots of techniques exist in literature to classify the medical images. The main drawback of traditional methods is the semantic gap that exists between the low-level visual information captured by imaging devices and high-level semantic information perceived by a human being. The difficulty of querying and managing the large datasets leads to a new mechanism called deep convolutional neural network. Deep learning techniques have recently achieved an impressive result in the field of computer vision along with Medical Engineering. In this paper, we proposed and evaluated a deep convolutional neural network, designed for classifying the Chest Diseases. The proposed model consists of Convolutional layers, ReLU Activations, Pooling layer, and Fully connected layer. Last full connected layer which is consists of fifteen output units. Each output unit will predict the probability of one of the fifteen diseases. A publicly available dataset called Chest X-Ray 14 which consists of fifteen classes named Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, Hernia and No Finding images used to train this model. Surprisingly this model gives a good result in multiclass classification. The average accuracy of 89.77% is achieved for the classification of different diseases. The comparative analysis shows the effectiveness of the proposed model. The proposed technique is best suited for classifying the multiclass medical images for different thorax diseases.