A federated approach for detecting the chest diseases using DenseNet for multi-label classification

Abstract

Multi-label disease classification algorithms help to predict various chronic diseases at an early stage. Diverse deep neural networks are applied for multi-label classification problems to foresee multiple mutually non-exclusive classes or diseases. We propose a federated approach for detecting the chest diseases using DenseNets for better accuracy in prediction of various diseases. Images of chest X-ray from the Kaggle repository is used as the dataset in the proposed model. This new model is tested with both sample and full dataset of chest X-ray, and it outperforms existing models in terms of various evaluation metrics. We adopted transfer learning approach along with the pre-trained network from scratch to improve performance. For this, we have integrated DenseNet121 to our framework. DenseNets have a few focal points as they help to overcome vanishing gradient issues, boost up the feature propagation and reuse and also to reduce the number of parameters. Furthermore, gradCAMS are used as visualization methods to visualize the affected parts on chest X-ray. Henceforth, the proposed architecture will help the prediction of various diseases from a single chest X-ray and furthermore direct the doctors and specialists for taking timely decisions.