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Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study
Mohamed Elhoseny1,2, Zahraa Tarek2, Ibrahim M El-Hasnony2
1College of Computing and Informatics, University of Sharjah, Sharjah, UAE.
Insights
This study introduces an artificial neural network (ANN) model optimized with a butterfly algorithm for accurate COVID-19 detection from chest X-rays and CT scans. The hybrid model achieved superior performance in identifying COVID-19 patterns compared to other methods.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Automated disease prediction is crucial for managing public health crises like COVID-19.
- Accurate and rapid diagnosis of COVID-19 is essential to reduce mortality rates.
- Challenges in COVID-19 diagnosis include viral mutation and the need for intelligent detection systems.
Purpose of the Study:
- To develop and evaluate a hybrid artificial neural network (ANN) model for automated COVID-19 detection using chest X-ray (CXR) and computerized tomography (CT) images.
- To optimize the ANN model's parameters using the butterfly optimization algorithm (BOA).
- To compare the proposed model's performance against established methods like AlexNet, GoogLeNet, and Support Vector Machine (SVM).
Main Methods:
- A hybrid model combining an artificial neural network (ANN) with parameter optimization via the butterfly optimization algorithm (BOA) was proposed.
- The model was trained and tested on six publicly available datasets comprising CXR and CT images.
- Performance was evaluated by comparing the proposed model against pretrained AlexNet, GoogLeNet, and SVM.
Main Results:
- The proposed hybrid ANN-BOA model demonstrated superior performance in recognizing COVID-19 patterns from medical images.
- The model achieved an average accuracy of 90.48%, outperforming SVM (81.09%), AlexNet (86.76%), and GoogLeNet (84.97%).
- The experimental results validated the model's effectiveness across both X-ray and CT imaging datasets.
Conclusions:
- The hybrid ANN-BOA model offers a highly accurate and effective approach for automated COVID-19 detection from CXR and CT scans.
- This intelligent detection system can significantly aid physicians in diagnosing COVID-19, leading to faster outcomes and potentially lower mortality rates.
- The study highlights the potential of optimized deep learning models in medical diagnostics for infectious diseases.
Abstract:
Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (COVID-19) has a significant effect on public health and the everyday lives of individuals currently residing in more than 100 nations. Despite effective attempts to reach an appropriate trend to forecast COVID-19, the origin and mutation of the virus is a crucial obstacle in the diagnosis of the detected cases. Even so, the development of a model to forecast COVID-19 from chest X-ray (CXR) and computerized tomography (CT) images with the correct decision is critical to assist with intelligent detection. In this paper, a proposed hybrid model of the artificial neural network (ANN) with parameters optimization by the butterfly optimization algorithm has been introduced. The proposed model was compared with the pretrained AlexNet, GoogLeNet, and the SVM to identify the publicly accessible COVID-19 chest X-ray and CT images. There were six datasets for the examinations: three datasets with X-ray pictures and three with CT images. The experimental results approved the superiority of the proposed model for cognitive COVID-19 pattern recognition with average accuracy 90.48, 81.09, 86.76, and 84.97% for the proposed model, support vector machine (SVM), AlexNet, and GoogLeNet, respectively.
