Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers’ outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.
Brain Cancer is quite possibly the most driving reason for death in recent years. Appropriate diagnosis of the cancer type empowers the specialists to make the right choice of treatment, decision and to save the patient's life. It goes no saying the importance of a computer-aided diagnosis system with image processing that can classify the tumor types correctly. In this paper, an enhanced approach has been proposed, that can classify brain tumor types from Magnetic Resonance Images (MRI) using deep learning and an ensemble of Machine Learning Algorithms. The system named BCM-VEMT can classify among four different classes that consist of three categories of Brain Cancers (Glioma, Meningioma, and Pituitary) and Non-Cancerous which means Normal type. A Convolutional Neural Network is developed to extract deep features from the MRI images. Then these extracted deep features are fed into multi-class ML classifiers to classify among these cancer types. Finally, a weighted average ensemble of classifiers is used to achieve better performance by combining the results of each ML classifier. The dataset of the system has a total of 3787 MRI images of four classes. BCM-VEMT has achieved better performance with 97.90% accuracy for the Glioma class, 98.94% accuracy for the Meningioma class, 98.00% accuracy for the Normal class, 98.92% accuracy for the Pituitary class, and overall accuracy of 98.42%. BCM-VEMT can have a great significance in classifying Brain Cancer types.
Brain Cancer is quite possibly the most driving reason for death in recent years. Appropriate diagnosis of the cancer type empowers the specialists to make the right choice of treatment, decision and to save the patient's life. It goes no saying the importance of a computer-aided diagnosis system with image processing that can classify the tumor types correctly. In this paper, an enhanced approach has been proposed, that can classify brain tumor types from Magnetic Resonance Images (MRI) using deep learning and an ensemble of Machine Learning Algorithms. The system named BCM-VEMT can classify among four different classes that consist of three categories of Brain Cancers (Glioma, Meningioma, and Pituitary) and Non-Cancerous which means Normal type. A Convolutional Neural Network is developed to extract deep features from the MRI images. Then these extracted deep features are fed into multi-class ML classifiers to classify among these cancer types. Finally, a weighted average ensemble of classifiers is used to achieve better performance by combining the results of each ML classifier. The dataset of the system has a total of 3787 MRI images of four classes. BCM-VEMT has achieved better performance with 97.90% accuracy for the Glioma class, 98.94% accuracy for the Meningioma class, 98.00% accuracy for the Normal class, 98.92% accuracy for the Pituitary class, and overall accuracy of 98.42%. BCM-VEMT can have a great significance in classifying Brain Cancer types.
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