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Paper details
Number 2 - June 2025
Volume 35 - 2025
AI enabled pneumonia detection and diagnosis based on the concatenation approach: A framework for healthcare sustainability
Prince Priya Malla, Sudhakar Sahu, Ryszard Tadeusiewicz, Paweł Pławiak
Abstract
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.
Keywords
artificial intelligence, healthcare, medical imaging, pneumonia detection, transfer learning, sustainability