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An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.
Batra, Shivani; Sharma, Harsh; Boulila, Wadii; Arya, Vaishali; Srivastava, Prakash; Khan, Mohammad Zubair; Krichen, Moez.
  • Batra S; Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad 201206, India.
  • Sharma H; Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad 201206, India.
  • Boulila W; Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia.
  • Arya V; RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia.
  • Srivastava P; School of Engineering, GD Goenka University, Gurugram 122103, India.
  • Khan MZ; Department of Computer Science and Engineering, Graphic Era (Deemed to Be University), Dehradun 248002, India.
  • Krichen M; Department of Computer Science and Information, Taibah University, Medina 42353, Saudi Arabia.
Sensors (Basel) ; 22(19)2022 Oct 02.
Article in English | MEDLINE | ID: covidwho-2066352
ABSTRACT
Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumothorax / COVID-19 Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Aged / Humans Country/Region as subject: North America Language: English Year: 2022 Document Type: Article Affiliation country: S22197474

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumothorax / COVID-19 Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Aged / Humans Country/Region as subject: North America Language: English Year: 2022 Document Type: Article Affiliation country: S22197474