Browsing by Author "Saini, Sumit"

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    Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification
    (2025) Saini, Sumit
    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.