sufyan zaben; Akbas Ezaldeen Ali
Abstract
Covid-19 is a deadly virus that has spread worldwide, causing millions of deaths. Chest X-ray is one of the most common methods of diagnosing the infection of Covid - 19. Therefore, this paper has presented an efficient method to detect Covid-19 through X-rays of the chest area through a Neural convolution ...
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Covid-19 is a deadly virus that has spread worldwide, causing millions of deaths. Chest X-ray is one of the most common methods of diagnosing the infection of Covid - 19. Therefore, this paper has presented an efficient method to detect Covid-19 through X-rays of the chest area through a Neural convolution network (CNN). the proposed system has used a convolution neural network to classify the extracted features. Since CNN needs a set of data defined for training and testing, the proposed method used a public dataset of 350 pneumonia x-ray images, 300 viral images, and 350 normal images for evaluation. Besides, the proposed work achieved a satisfactory accuracy of 95% based on the X-ray image.
Communication
Nada Hussain; Matheel Abdulmunim; Akbas Ezaldeen Ali
Abstract
Auto-Correction is the process of correcting a misspelled word typed by the user as an application of automated translation process. lip-reading is the process of recognizing the words through processing and observing the visual lip movement of a speaker’s talking without any audio input. Although ...
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Auto-Correction is the process of correcting a misspelled word typed by the user as an application of automated translation process. lip-reading is the process of recognizing the words through processing and observing the visual lip movement of a speaker’s talking without any audio input. Although visual information itself cannot be considered as enough resource to provide normal speech as intelligibility, it may succeed with several cases especially when the words to be recognized are limited. Auto-correction is a trail to diminish the number of errors that can be generated by lip reading systems and to improve their accuracy, many error-correction techniques were visualized. In this paper an auto- correction model is proposed to correct the misspelled words recognized by a lip reading system, the output of a lip reading system is subjected to auto-correction model to enhance the accuracy of the system. The auto-correction model is based on levenshtien distance and dictionary lookup with a proposed dataset. The proposed model achieved accuracy of more than 67% enhancing the lip reading system by almost 30%.