Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model

Abstract

Population size has made disease monitoring a major concern in the healthcare system, due to which auto-detection has become a top priority. Intelligent disease detection frameworks enable doctors to recognize illnesses, provide stable and accurate results, and lower mortality rates. An acute and severe disease known as Coronavirus (COVID19) has suddenly become a global health crisis. The fastest way to avoid the spreading of Covid19 is to implement an automated detection approach. In this study, an explainable COVID19 detection in CT scan and chest X-ray is established using a combination of deep learning and machine learning classification algorithms. A Convolutional Neural Network (CNN) collects deep features from collected images, and these features are then fed into a machine learning ensemble for COVID19 assessment. To identify COVID19 disease from images, an ensemble model is developed which includes, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). The overall performance of the proposed method is interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM), and t-distributed Stochastic Neighbor Embedding (t-SNE). The proposed method is evaluated using two datasets containing 1,646 and 2,481 CT scan images gathered from COVID19 patients, respectively. Various performance comparisons with state-of-the-art approaches were also shown. The proposed approach beats existing models, with scores of 98.5% accuracy, 99% precision, and 99% recall, respectively. Further, the t-SNE and explainable Artificial Intelligence (AI) experiments are conducted to validate the proposed approach.

Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN

“…A confusion matrix is used to demonstrate the corresponding results. It is a tool to show correct and incorrect classification results to explain which classes testing images are miss-classified [38].…”

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

“…Ullah F. et.al. [40] contributed in detection and classification Covid-19 in CT and X-ray images. Ravi V. et.al.…”

“…One article collected data From D. S. Kermany, et al, "Identifying medical diagnoses and treatable diseases by image-based deep learning", J. P. Cohen, P. Morrison, and L. Dao, "Covid-19 image data collection" [45]. Another one from Git hub, Italian Society of Medical and Interventional Radiology, Radiological Society of North America (RSNA), Radiopaedia, and SIRM) and Kaggle repository [52].…”

“…[53] -527 images [52] From Git hub, Italian Society of Medical and Interventional Radiology, Radiological Society of North America (RSNA), Radiopaedia, and SIRM) and Kaggle repository.…”

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

“…Even though many studies [ 14 , 15 , 16 , 17 , 18 , 19 ] have produced information regarding COVID-19 by using the concept of big data, machine learning and artificial intelligence, studies related to the prediction of mortality using different AI-driven models are rare globally and in the region. Based on our search, no study has reported on the use of AI-driven ensemble modelling to predict mortality due to COVID-19 in the region.…”