ABSTRACT
Explainable Artificial Intelligence is a key component of artificially intelligent systems that aim to explain the classification results. The classification results explanation is essential for automatic disease diagnosis in healthcare. The human respiration system is badly affected by different chest pulmonary diseases. Automatic classification and explanation can be used to detect these lung diseases. In this paper, we introduced a CNN-based transfer learning-based approach for automatically explaining pulmonary diseases, i.e., edema, tuberculosis, nodules, and pneumonia from chest radiographs. Among these pulmonary diseases, pneumonia, which COVID-19 causes, is deadly; therefore, radiographs of COVID-19 are used for the explanation task. We used the ResNet50 neural network and trained the network on extensive training with the COVID-CT dataset and the COVIDNet dataset. The interpretable model LIME is used for the explanation of classification results. Lime highlights the input image's important features for generating the classification result. We evaluated the explanation using radiologists' highlighted images and identified that our model highlights and explains the same regions. We achieved improved classification results with our fine-tuned model with an accuracy of 93% and 97%, respectively. The analysis of our results indicates that this research not only improves the classification results but also provides an explanation of pulmonary diseases with advanced deep-learning methods. This research would assist radiologists with automatic disease detection and explanations, which are used to make clinical decisions and assist in diagnosing and treating pulmonary diseases in the early stage.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID-19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS-CoV-2 and related ß coronaviruses Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV, bat coronavirus (BAT-CoV) were comprehensively analyzed using a custom-built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole-genome sequence-based phylogeny revealed that the strains of SARS-CoV-2 are more similar to the BAT-CoV strains. The mutational analysis showed that on average MERS-CoV and BAT-CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS-CoV genome differed at 26.64 sites from the reference genome of SARS-CoV-2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS-CoV and SARS-CoV-2 (106.8 and 107, respectively), and a lower number for SARS-CoV and BAT-CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected ß viral genomes revealed that the newly born SARS-COV-2 is closely related to BAT-CoV, whereas, MERS-CoV is more distinct from the SARS-CoV-2 than BAT-CoV and SARS-CoV.