Low Powered Models for Disease Detection and Classification for Radiology Images

The goal of this project is to develop models and train them on various Radiology images. These models need to be made suitable to be run on low powered devices such as ARM devices and Android devices. For this, they will have to undergo quantization and pruning (during training as well as after training). A hybrid approach of these techniques will be experimented to generate substantial results.