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Multimodal Disease Detection Using Chest X-Ray Images and Electrocardiogram Signals
6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021 ; 425:481-490, 2022.
Article in English | Scopus | ID: covidwho-1899082
ABSTRACT
Since the arrival of novel COVID-19 disease, lack of early detection by radiologists and medical specialists of various nations is a notable cause for the block for initial investigation of heart complications. The consolidated computer-aided diagnosis (CAD) systems are required for accurate prediction and early monitoring using multiple biomedical data. This multimodal CAD system is mainly required to reduce the mortality rate caused by inappropriate treatment. To address this problem, a novel automatic multimodal disease detection system has been proposed using patient chest X-ray images and heart electrocardiogram (ECG) data. Since this work aims to observe lung infection and heart irregularities, simultaneously, therefore, we propose a deep learning framework for multimodal disease detection. It consists of preprocessing, automated feature extraction, and classification. The raw chest X-ray images and ECG signals are preprocessed using multiple filtering techniques. For automatic feature extraction, we have designed a convolutional neural network (CNN) for preprocessed X-ray images and ECG signals. We have designed the long short-term memory (LSTM) classifier for each biomedical input. The outcome of the classifier can be used to provide the consolidated medical judgment for further investigations. Experimental results show the efficiency of the proposed multimodal CAD system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021 Year: 2022 Document Type: Article