ABSTRACT:
This study introduces a novel framework for automated lung disease detection, leveraging deep learning methodologies on X-ray and CT scan images. The workflow encompasses dataset collection, rigorous preprocessing, optimization techniques, and feature extraction strategies. Notably, Ant Colony Optimization is integrated to refine model parameters effectively. Employing Convolutional Neural Networks, the framework achieves robust disease classification, offering reliable predictions crucial for timely diagnosis and treatment planning. This work signifies a significant breakthrough in medical image analysis, providing a sophisticated and automated approach to lung disease detection.


OBJECTIVE:
The primary objective of this study is to develop a novel framework for automated lung disease detection using deep learning techniques applied to X-ray and CT scan images. This framework aims to streamline the process of disease detection through comprehensive steps, including dataset collection, preprocessing, optimization, and feature extraction. Specifically, the study seeks to integrate Ant Colony Optimization to enhance model refinement and leverage Convolutional Neural Networks for robust disease classification. The ultimate goal is to provide clinicians with reliable predictions crucial for timely diagnosis and treatment planning, thereby advancing the field of medical image analysis and offering a sophisticated and automated approach to lung disease detection.