ViBaNet: A Novel Deep Learning Approach to Detect Bacterial and Viral Pneumonia

Viral and bacterial pneumonia mostly occurs in lungs of the humans and are life-threatening diseases if timely treatment is ignored. In this regard, age is a very crucial aspect as the effects are different in the age of different people. Most commonly, infants, as well as aged people are at risk due to these diseases as their lungs are badly affected. It is a really challenging, and time-consuming task for diagnostician to inspect the lung’s radiographic scans and diagnose pneumonia. Binary classification such as normal vs pneumonia or normal vs bacterial pneumonia is simple, however, detecting bacterial and viral pneumonia is a tricky and difficult task. Therefore, we implemented a deep-learning method to identify pneumonia victims effectively by employing lung X-rays to avoid wrong decisions by radiologists. In the present study, we proposed a novel deep-learning technique, ViBaNet, which is based on the customized residual neural network to investigate the validity of ViBaNet to diagnose complicated pneumonia disorders. We conducted experiments employing the uncustomed and ViBaNet and evaluated the efficacy of these techniques. The novel ViBaNet obtained an accuracy, precision, recall, and F1-score of 92.56%, 95.65%, 96.35%, and 96%, respectively. The above-mentioned analysis results provide evidence that the ViBaNet is effective to be utilized to identify pneumonia individuals. Moreover, the comparison of ViBaNet with other techniques has shown superior performance results using the augmented data.