Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification
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Date
2025
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Abstract
Pneumonia is an infectious disease characterized by inflammation of the lungs’ air sacs, which results in the
accumulation of fluid or pus. Medical images is important for the timely identification and precise diagnosis of
illnesses; chest X-rays are a commonly utilized modality for respiratory disorders including pneumonia. In this
research, optimized double transformer residual super-resolution network-related chest x-ray imageries for the
classification of pneumonia identification (DTRSN-XRI-CPI). The procedure involves pre-processing the input
image using region-aware neural graph collaborative filtering (RNGCF) to reduce noise, enhance contrast, and
eliminate high and low frequencies from the collected dataset. Next, the Synchro-squeezed fractional wavelet
transform (SFWT) is utilized for the feature extraction to extract color features such as color, shape, spatial,
texture, and relation from the image. Hence, the weight parameters for DTRSN are optimized using the Hunter
Prey Optimization Algorithms (HPOA). Then the DTRSN-XRI-CPI is implemented in Python and the performance
metrics like precision, accuracy, recall, specificity, F1-score, and ROC are analysed. The performance of the
DTRSN-XRI-CPI approach attains 20.7 %, 22.6 % and 30.5 % higher accuracy; 21.8 %, 29.3 % and 30.5 %higher
precision and 21.8 %, 29.5 % and 32.6 % higher recall when analysed through existing an intelligent compu
tational framework based on deep learning for the identification and classification of pneumonia illness (ICPD-
DL-ICF), an adaptive and altruistic deep feature selection approach based on PSO for pneumonia detection from
chest X-rays (APSO-DFSM-PDCX) and a deep learning system that uses explainable AI (DLAIB-PI-EAI) techniques
respectively.