ABSTRACT
Deep Learning, especially Convolutional Neural Net-works (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning, and optimization of the models better results can be achieved. This work aims in contributing to filling this void in the literature. On this aspect, this work explores and designs both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50, and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID-19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements. © 2022 IEEE.
ABSTRACT
IoT and Machine Learning has improved multi-fold in recent years and they have been playing a great role in healthcare systems which includes detecting, screening and monitoring of the patients. IoT has been successfully detecting different heart diseases, Alzheimer disease, helping autism patients and monitoring patients’ health condition with much lesser cost but providing better efficiency, reliability and accuracy. IoT also has a great prospect in fighting against COVID-19. This chapter discusses different aspects of IoT in aiding healthcare systems for detecting and monitoring Coronavirus patients. Two such IoT based models are also designed for automatic thermal monitoring and for measuring and real-time monitoring of heart rate with wearable IoT devices. Convolutional Neural Networks (CNN) is a Machine Learning algorithm that has been performing well in detecting many diseases including Coronary Artery Disease, Malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Detecting Corona positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two CNN models with different number of convolution layers and three other models based on ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. The proposed model performs with an accuracy of 97.5% and a precision of 97.5%. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.975 and F1-score of 97.5. It can be improved further by increasing the dataset for training the model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.