Transformation with Yolo Tiny Network Architecture for Multimodal Fusion in Lung Disease Classification

Abstract

This article focuses on the crucial task of identifying lung diseases through the analysis and processing of medical images, aiming to aid medical professionals in the diagnostic process. Image fusion technologies, employed in prospective remedies, enhance diagnostic precision and reliability by amalgamating radiological information from diverse sources. The study introduces a Yolo Tiny network-based medical imaging fusion technique, yielding fused images with enhanced clarity and diverse physical information. The proposed method involves the integration of an adaptive pre-processing module, YOLO Tiny for efficient classification, discrete wavelet transform for feature combination, and a novel adaptive image fusion technique based on a classifier, contributing to improved diagnostic accuracy. Addressing challenges like uneven illumination and poor contrast in medical images, the comprehensive workflow enhances image quality, facilitating effective classification in medical imaging. Experimental results showcase the efficacy of the proposed scheme in CT imaging signs retrieval and classification, specifically for lung nodule detection. The system exhibits high accuracy, specificity, sensitivity, and recall, outperforming existing systems in terms of SSIM, PSNR, MSE, and overall accuracy for both single-modal and multi-modal datasets. The incorporation of YOLO and YOLO Tiny networks underscores improved performance in image processing and classification tasks across diverse medical imaging modalities. The hybrid model, suggested in this study, effectively preserves rich specific information from source images, yielding esthetically pleasing visuals for medical practitioners and enhancing the diagnostic process.