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Explainable Machine Learning Techniques in Medical Image Analysis Based on Classification with Feature Extraction

Author(s)
B. Dwarakanath Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, India
Gitanjali Shrivastava Department of Law, Symbiosis Law School, Pune, India
Rohit Bansal Department of Management Studies, Vaish College of Engineering, Rohtak, Haryana, India
Praful Nandankar Department of Electrical Engineering, Government College of Engineering, Nagpur, India
Veera Talukdar Registrar, Kaziranga University, Jorhat, Assam, India
M Ahmer Usmani Department of Computer Science and Engineering, Bharati Vidyapeeth Deemed to be University, Department of Engineering and Technology, Navi Mumbai, India
Abstract
Animals are also afflicted by COVID-19, a virus that is quickly spreading and infects both humans and animals. This fatal viral disease has an impact on people's daily lives, health, and economy of a nation. Most effective machine learning method is deep learning, which offers insightful analysis for examining a significant number of chest x-ray pictures that have a significant bearing on COVID-19 screening. This research proposes novel technique in lung image analysis for detection of lung infection due to COVID using Explainable Machine learning techniques. Here the input has been collected as COVID patient’s lung image dataset and it has been processed for noise removal and smoothening. This processed image features have been extracted using spatio transfer neural network integrated with DenseNet+ architecture. Extracted features has been classified using stacked auto Boltzmann encoder machine with VGG-19Net+. With the transfer learning method integrated into the binary classification process, the suggested algorithm achieves good classification accuracy. The experimental analysis has been carried out for various COVID dataset in terms of accuracy, precision, Recall, F-1score, RMSE, MAP. The proposed technique attained accuracy of 95%, precision of 91%, recall of 85%, F_1 score of 80%, RMSE of 61% and MAP of 51%.
Publication Details
Page(s) 342-357
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 3, Year: 2022

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Keywords
Classification, lung infection, Feature extraction., COVID -19, Explainable Machine learning, lung image analysis
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