XHDLNet Classification of Virus-Borne Diseases for Chest X-Ray Images Using a Hybrid Deep Learning Approach

Abstract

Various forms and symptoms of corona virus have been observed in human body especially in heart, chest and affects the respiratory system. In the initial phase, RT-PCR examination is applied to monitor the target disease, but suffers from low sensitivity and a laborious process. Apart from this, another mechanism for corona virus detection involves the analysis the CT image has become an imperative device for clinical judgment. However, manual investigation of such disease in numerous amounts of images is not the optimal approach. Additionally, recent advancement in artificial intelligence techniques have assisted medical diagnosis to identify the virus in a standard environment. In this work, the potential of such intelligence methods is analyzed and extended by considering the optimal feature extraction capability and proposes a hybrid approach in which three universal architectures namely: Inception V4, DenseNet 201 and Xception have been utilized which not only classify the corona virus disease but may also provide a pathway to apply similar method in other medical diagnosis.