Efficient NetV2 for Lung Disease Diagnosis and Treatment by Harnessing the Synergy of Verifiable Convolutional Neural Network optimized with Nutcracker Optimizer Algorithm

Abstract

The growing demand for advanced computer-aided diagnosis systems in medical imaging is increasing for accurately detecting diseases, like COVID-19, pneumonia, tuberculosis and lung nodules. In spite of advancements, a research gap remains in developing models that offer high precision and transparent decision-making. To address this, this paper proposes an EfficientNetV2 for Lung Disease Diagnosis and Treatment by Harnessing the Synergy of Verifiable Convolutional Neural Network optimized with Nutcracker Optimizer Algorithm (EN-LDD-VCNN). Here, the images taken from Chest CT-Scan dataset are used. Then High Accuracy Distributed Kalman Filter (HADKF) is used for image resizing and pixel normalization. Afterwards, the pre-processing images are given into the Verifiable Convolutional Neural Network with EfficientNetV2 (VCNN-ENetV2) for classifying lung diseases, like adenocarcinoma, large cell carcinoma (LCC), squamous cell carcinoma (SCC), normal. Finally, the Nutcracker Optimizer Algorithm (NOA) is employed to optimize the weight parameters of VCNN. The performance metrics like accuracy, precision, recall, error rate, Matthew’s Correlation Coefficient (MCC), ROC is analyzed. The proposed technique achieves 20.73%,13.79% and 16.47% higher accuracy; 14.44%, 34.28% and 24.14% higher MCC; 12.16%, 18.39% and 26.27% higher precision compared with the existing techniques: Deep learning-dependent method to diagnose lung cancer by using CT-scan images (DL-DLC-CTI), Attention Enhanced Inception NeXt dependent Hybrid Deep Learning Method for Lung Cancer Diagnosis (AEI-HDLM-LCD) and Detection with categorization of lung disorders utilizing machine learning and deep learning strategies for pneumonia and Covid-19 (DC-LD-PC-DL) respectively.