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@article{191728,
        author = {Soumya Katiyar},
        title = {Automatic Detection of Lungs Infection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {68-77},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191728},
        abstract = {LUNG’S INFECTION is considered to be the most dangerous and fatal disease in the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly worldwide, he is thought to have come from Wuhan in China and has a great responsibility death toll. In this project, I proposed an automatic detection method for coronavirus infection based on X-ray images. Since, there are many detection techniques already been proposed based on Convolutional Neural Networks (CNN). But these techniques and analysis many times failed to detect in an unknown image and accuracy is also a major issue. In this project, we will analyse the different detection methods such as Neural Networks (NN), K-Nearest Neighbour (KNN), Decision Trees, Random Forest for the detection of the virus and also implement detection making the use of Convolution Neural Networks and pre-trained models for feature extraction based on deep learning. We will be doing semantic analysis for such viruses. The results showed that the extractor-classifier pair with the best performance is DarkNet architecture with softmax as classifier, which achieves the accuracy of 96.82%. Thus, the results provides a chance of virus and a comparative analysis based on performance, accuracy.},
        keywords = {LUNG’S infection, X-Ray images, Machine Learning (ML), Deep Learning (DL), Convolution Neural Network (CNN), Decision Tree, K-Nearest Neighbour (KNN), Random Forest.},
        month = {},
        }