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SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral., (© 2021. The Author(s).)

BACKGROUND: Polyps are tumorous growths in the colon or rectal area which can turn into cancer at later stages, thus detection of polyps is very important for timely prevention of colorectal cancer. The aim of the study is to develop a framework to accurately detect polyp images in colonoscopy images. OBJECTIVE: Development of an intelligent framework for classification of colorectal cancer from colon and rectal images. The standard machine learning, convolutional neural networks and ensemble models with nature inspired approach were implemented for this study. Model optimization was performed by varying hyper parameters. The main objective was to find an optimal model with high accuracy, optimized weights and less parameters. METHODS: The deep learning Convolutional Neural Network (CNN) models such as VGG19, ResNet50, EfficientNet, Ensemble Model (EM), and Modified Ensemble CNN with Genetic Algorithm (MEGANET) were implemented for the classification of colon images. RESULTS: Ensemble model was also created with two best performing deep learning models to further achieve higher accuracy of 96%. The ensemble model outperformed the other models in terms of accuracy, precision, recall, and F1 score. But this model has more complexity. The MEGANET, nature inspired evolutionary ensemble CNN model was implemented with transfer learning and genetic algorithms for weights optimization and parameter reduction. It achieved accuracy of 95%, on training data. CONCLUSION: The MEGANET performed similar to EM with less number of parameters on training, validation and test dataset. In future different methods will be implemented to further reduce the parameters and attain reasonable accuracy using MEGANET. [ABSTRACT FROM AUTHOR]

With the onset of pandemics and accrescent stress among people, heart disease has become one of the leading contributors to premature deaths worldwide. Early prognosis of this disorder is the most viable strategy for increasing the prospects of individuals' survival. Numerous methods exploiting machine learning algorithms for heart disease prediction have been reported in the literature, but they all suffer from overfitting problems. Conspicuously, to improve the prediction accuracy, in this research work, an efficient meta‐heuristic‐based feature selection technique, namely NSGA‐II, is employed. The proposed solution aims to reduce the feature set and thus improve the prediction accuracy supported by intelligent machine learning models. The presented classifier is trained and validated using a comprehensive heart disease dataset by utilizing NSGA‐II selected features with 10‐fold cross‐validation. For performance validation, the results of the proposed model are compared with the state‐of‐the‐art machine learning algorithms, such as a k‐nearest neighbour, support vector machine, Bayesian belief networks, random forest, and naive bayes. The simulation results highlight the improved performance of the proposed model with NSGA‐II and achieve a high prediction accuracy of 97.32%. Furthermore, results of sensitivity (92.84%), specificity (92.60%), precision (91.25%), and F‐measure (92.17%) prove the utility of the proposed approach for heart disease prediction over all other variants. [ABSTRACT FROM AUTHOR]