AI enabled pneumonia detection and diagnosis based on the concatenation approach: A framework for healthcare sustainability

• Autorzy: Malla Prince Priya , Sahu Sudhakar , Tadeusiewicz Ryszard , Pławiak Paweł

Identyfikatory

DOI

10.61822/amcs-2025-0024

• Języki publikacji: EN

Abstrakty

EN

Early detection and diagnosis of pneumonia play a significant role in saving human life. However, detection of pneumonia from chest X-ray images with the help of radiologists is a time-consuming task. Thus, the development of an appropriate artificial intelligence (AI) enabled model for the precise detection of pneumonia becomes an important research topic. In this aspect, we develop an automated transfer learning-based pneumonia detection framework using a feature concatenation approach. The proposed approach uses the DenseNet pre-trained network and concatenates the features extracted from several dense blocks of DenseNet in order to obtain the dense multiscale information from the chest X-ray images. This feature concatenation process helps in improving the classification accuracy of the proposed framework and simplifies the pneumonia detection process. The proposed work achieves accuracy, sensitivity, specificity, and precision of 98.60%, 97.03%, 99.14%, and 97.51%, respectively, on the chest X-ray pneumonia dataset which are superior results to the existing deep learning-based pneumonia frameworks. It is concluded that the proposed AI-enabled pneumonia detection framework has the prospective to be considered as a computer-aided diagnosis support system for the early diagnosis of pneumonia.

Słowa kluczowe

EN

artificial intelligence; healthcare; medical imaging; pneumonia detection; transfer learning

PL

sztuczna inteligencja; opieka zdrowotna; obrazowanie medyczne; wykrycie zapalenia płuc; uczenie transferowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2025
• Tom: Vol. 35, no. 2
• Strony: 341--355
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Malla Prince Priya

• School of Electronics Engineering, Kalinga Institute of Industrial Technology, Patia,751024, Bhubaneswar, India

autor

Sahu Sudhakar

• School of Electronics Engineering, Kalinga Institute of Industrial Technology, Patia,751024, Bhubaneswar, India

autor

Tadeusiewicz Ryszard

• Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, ul. Mickiewicza 30, 30-059 Krakow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155 Krakow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Baltycka 5, 44-100 Gliwice, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).