Disease detection on medical images using light-weight Convolutional Neural Networks for resource constrained platforms

Abstract

This paper presents a thorough investigation to optimize light-weight convolutional neural networks for diseases detection from medical images. Particularly, we are interested in building an efficient recognition convolutional deep learning architecture running on portable, energy efficient, resources constrained platforms, as aids to medical personnel. Several state of the art light-weight deep neural network architectures as well as more complex (for better performance) ones were compared not only from the point of view of recognition accuracy but also from the complexity perspective. An original solution with Android and Raspberry-Pi implementations is proposed to give a good trade-off between complexity, detection accuracy, latency and portability in building intelligent medical instruments. Experiments on the Chest X-ray dataset indicate that our proposed solutions achieves state of the art accuracies with 91.22% less trainable parameters and with 3.62 times less memory usage storage, when compared with other state of the art light weight models.