Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.
Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.
Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN) model is proposed in this work to classify the infected cases. MCNN focuses on detecting infected cells, which aids in the computation of parasitemia, or infection measures. The proposed model achieved 0.9929, 0.9848, 0.9859, 0.9924, 0.0152, 0.0141, 0.0071, 0.9890, 0.9894, and 0.9780 in terms of specificity, sensitivity, precision, accuracy, F1-score, and Matthews Correlation Coefficient, respectively. A comparison was carried out between the proposed model and some recent works in the literature. This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.
Instance segmentation is a challenging computer vision task that requires the prediction of object instances and their per-pixel segmentation mask. This makes it a hybrid of semantic segmentation and object detection. It detects and delineates each distinct object of interest appearing in an image. Mask R-CNN model is common for instance segmentation that has several versions for improving this task. We proposed a simple comparison between Fifteenth different version frameworks from Mask-RCNN for object instance segmentation. Our survey representing the difference between the popular versions ofMask R-CNN. The Mask R-CNN method extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The results in most versions were implemented on of the COCO dataset that created for instance segmentation tasks.
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