Lung Nodule Detection Using Vision Transformer with Avian Optimization

Project Code :TCMAPY1119

Objective

The primary objective of this project is to develop and evaluate a novel diagnostic tool based on Vision Transformer (ViT) networks for the accurate and efficient detection and localization of lung nodules in chest CT scans. By incorporating three-dimensional analysis capabilities and optimizing the training process with the Avian optimization algorithm, this project aims to achieve a balanced performance between sensitivity and specificity, two crucial metrics for the effective diagnosis of lung diseases, including cancer. Through this innovative approach, the project seeks to significantly improve the field of medical image analysis, contributing to the advancement of computer-aided diagnosis systems and ultimately facilitating the early diagnosis and treatment of lung diseases. This research endeavors to harness the potential of deep learning and optimization algorithms to address the challenges posed by the complexity of lung nodule detection, paving the way for future developments in precision medicine and healthcare technology.

Abstract

The timely and accurate identification of lung nodules in chest CT images is essential for the diagnosis of lung diseases, with a particular emphasis on cancer. This study introduces an innovative approach that utilizes Vision Transformer (Vit) networks for the three-dimensional analysis of chest CT scans. To enhance training efficiency, the Avian optimization algorithm is applied. The model is rigorously evaluated using benchmark datasets, demonstrating its proficiency in precisely detecting and localizing lung nodules. The emphasis is on achieving a balanced performance between sensitivity and specificity, crucial metrics for effective diagnostic tools.

This research highlights the potential of Vit networks for complex lung nodule diagnosis, making significant improvements to the field of medical image analysis. Furthermore, the efficiency of Avian optimization strengthens the model, paving the way for advanced early diagnosis of lung diseases through cutting-edge computational methodologies.

Keywords: Lung nodule detection, Deep Learning, Vision Transformer networks, CT image analysis, Avian optimization, Computer-aided diagnosis.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.