Deep learning based medical X-ray image recognition and classification

Abstract

Analysis of radiology images are mostly being done by medical specialists, as it is a critical sector and people expect highest level of care and service regardless of cost. Though, it is quite limited due to its complexity and subjectivity of the images. Extensive variation exists across different interpreters and fatigue in terms of image interpretation by human experts. Our primary objective is to analyze medical X-ray images using deep learning and exploit images using Pandas, Keras, OpenCV, TensorFlow etc. to achieve classification of diseases like Atelectasis, Consolidation, Cardiomegaly, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pleural, Pneumonia, Pneumothorax, Thickening etc. We have used Convolutional Neural Networks (CNN) algorithm because CNN based deep learning classification approaches have ability to automatically extract the high level representations from big data using little pre-processing compared to other image classification algorithms. Ultimately, our simple and efficient model will lead clinicians towards better diagnostic decisions for patients to provide them solutions with good accuracy for medical imaging. Keywords: Convolutional Neural Networks (CNN), X-ray, Deep Learning, Pandas, Keras, Radiography, TensorFlow, OpenCV and Artificial Intelligence.