Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for lung disease detection system in IoT

Pneumonia causes a high rate of newborn morbidity and mortality. The challenge is accurately identifies respiratory disorders while overcoming the limitations of existing technologies such as low accuracy, delayed response, and restricted scalability. To overcome this complication, Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for Lung Disease Detection system in IoT (LDD-VONN-CXR-IoT) is proposed. Initially, input CXR images are gathered from chest-X-ray Dataset. Then, pre-process the input CXR images using Two-way Recursive filtering (TWRF) for normalizing image and increasing the quality of the images. Afterwards, the preprocessed image is supplied to the feature extraction. Adaptive Synchro Extracting Transform (ASET) is employed to extract the statistical features. Finally, the extracted features are fed into Variational Onsager Neural Networks (VONN) which classifies the input CXR image into normal and pneumonia. The Golden Search Optimization Algorithm (GSOA) is used to optimize VONN that accurately detects the Lung Disease. The proposed LDD-VONN-CXR-IoT method is implemented. The performance metrics, like precision, accuracy, F1-score, Sensitivity, specificity, Error rate, ROC, computational time are examined. The proposed LDD-VONN-CXR-IoT approach attains 99.57%, 98.46%, and 98.13% for accuracy, F1 score, and precision respectively. These outcomes prove that this method for the Lung Disease Detection system in IoT is effectual tool to assist in clinical diagnosis. This method allows expertise to acquire exact results, thus providing the proper treatment.