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The COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerasechain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used.Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

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X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.