INTEGRATIVE APPROACHES ON ARTIFICIAL INTELLIGENCE AND DEEP LEARNING TECHNIQUES FOR LUNG CANCER IDENTIFICATION: A REVIEW OF BEST PRACTICES WITH RESEARCH OBJECTIVES

Abstract

Lung cancer is a significant global health concern, and accurate identification plays a crucial role in its diagnosis and treatment. The emergence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques has shown promising potential in improving lung cancer identification from CT/MRI images. This study aims to develop and optimize AI, ML, and DL methods to address the challenges faced by doctors and clinicians in accurately identifying lung cancer. The research objectives include developing AI models tailored for lung cancer identification, integrating heterogeneous data sources, enhancing interpretability and explainability of AI models, addressing class imbalance and limited data, improving early detection and risk assessment, ensuring model robustness and generalization, building real-time decision support systems, and evaluating and validating the performance of developed models. The proposed study will employ various AI, ML, and DL techniques to analyze medical images. By leveraging these techniques, the study aims to improve the accuracy and efficiency of lung cancer identification, enabling early detection, personalized treatment decisions, and improved patient outcomes. The research outcomes have the potential to transform lung cancer management and contribute to advancements in the field of AI-assisted diagnostics. Through extensive evaluation and validation using diverse datasets, the performance of the developed AI models will be assessed in terms of sensitivity, specificity, accuracy, and other relevant metrics. The clinical utility and impact of the AI models will be evaluated in real-world settings, considering factors such as healthcare efficiency, cost-effectiveness, and patient outcomes.