Multiclass Chest Disease Classification Using Deep CNNs with Bayesian Optimization

Ever since its outbreak, numerous research studies have been initiated worldwide as an attempt for an accurate and efficient diagnosis of COVID-19. In the recent past, patients suffering from various chronic lung diseases, either passed away due to COVID-19 or Pneumonia. Both of these pulmonary diseases are strongly correlated as they share a common set of symptoms and even for medical professionals, it has been difficult to perform discerned diagnosis for both of these diseases. The dire need of the current scenario is a chest-disease diagnosis framework for accurate, precise, real-time and automatic detection of COVID-19 because of its mass fatality rate. The review of various contemporary and previous research works show that the currently available computer-aided diagnosis systems are insufficient for realtime implementation of COVID-19 prediction due to their long training time, substantial memory requirements and excessive computations. This work proposes an optimized hybrid DNN-ML framework by combining Deep Neural Networks' (DNNs) models and optimized Machine Learning (ML) classifiers along with an efficacious image preprocessing approach. For feature extraction, Deep learning (DL) models namely GoogleNet, EfficientNetB0, and ResNet50 have been deployed and extracted features have been further fed to Bayesian optimized ML classifiers. The two major contributions of this study are, Edge based Region of Interest (ROI) extraction and use of Bayesian optimization approach for configuring optimal architectures of ML classifiers. With extensive experimentation, it has been observed that the proposed optimized hybrid DNN-ML model with encapsulated image preprocessing techniques performed much better as compared to various previously existing ML-DNN models. Based on the promising results obtained from this proposed light weight hybrid framework, it has been concluded that, this model can facilitate radiologists, while functioning as an accurate disease diagnosis and support system for early detection of COVID-19 and Pneumonia.