Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.
Automated detection of lung abnormalities has a significant role in the computer aided diagnosis of lung diseases. Recently, medical image analysis utilizes Convolution Neural Network(CNN) to improve the outcome of clinical diagnosis. In this paper, we propose customized CNN based multi-class lung abnormality classifier from CT images. The custom CNN is trained and tested using CT images showing lung abnormalities of Carcinoma, Fibrosis, Necrosis and their performance is also compared with the results using VGG16 and VGG19. It is found that the our Custom CNN shows good results for Carcinoma, Fibrosis, Healthy, Inflammation and Necrosis with classification accuracy of 0.912 compared to VGG16 and VGG19 with accuracy of 0.7435 and 0.7216 respectively. Hence, it is proven that our custom CNN can be utilized as a second opinion to radiologist expert and improving mortality rate of these lung diseases by providing class-specific treatment for the patients.
In this study, we propose a Gated Recurrent Unit (GRU) model to restore the following features: word and sentence boundaries, periods, commas, and capitalisation for unformatted English text. We approach feature restoration as a binary classifcation task where the model learns to predict whether a feature should be restored or not. A pipeline approach is proposed, in which only one feature (word boundary, sentence boundary, punctuation, capitalisation) is restored in each component of the pipeline model. To optimise the model, we conducted a grid search on the parameters. The effect of changing the order of the pipeline is also investigated experimentally; PERIODS > COMMAS > SPACES > CASING yielded the best result. Our fndings highlight several specifc action points with optimisation potential to be targeted in follow-up research.
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