Authors:

• Lakshmanaprabu S.K., B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
• Sachi Nandan Mohanty, Gandhi Institute for Technology, Bhubaneswar, India
• Shankar K, Kalasalingam Academy of Research and Education, Krishnankoil, India
• Arunkumar N, SASTRA University, Tanjavur, India
• Gustavo Ramirez, University of Cauca, Colombia

Introduction

Lung cancer remains one of the deadliest forms of cancer globally, accounting for a significant proportion of cancer-related deaths. Early diagnosis is crucial to improving survival rates, yet detecting malignant nodules in their early stages using traditional diagnostic methods is challenging. This study proposes an innovative AI-powered solution that leverages deep learning techniques to enhance the accuracy and efficiency of lung cancer diagnosis from CT images. The system's goal is to optimize classification performance, helping clinicians make more informed and timely decisions.

Methodology

The study presents a novel approach using an Optimal Deep Neural Network (ODNN) integrated with Linear Discriminant Analysis (LDA) to classify lung nodules as benign or malignant. The process begins by extracting deep features from CT scan images of the lungs, leveraging the powerful representational capabilities of deep neural networks. These extracted features undergo dimensionality reduction using LDA, which helps minimize computational complexity while retaining essential discriminative information for accurate classification.

What sets this study apart is the optimization of the ODNN model using a Modified Gravitational Search Algorithm (MGSA). MGSA is employed to fine-tune the network's parameters and improve classification performance. By optimizing hyperparameters such as learning rates and weights, MGSA ensures that the neural network operates at its full potential, leading to better predictions of lung cancer status.

Results

The AI-based model demonstrates impressive results, achieving a sensitivity of 96.2%, a specificity of 94.2%, and an overall accuracy of 94.56%. These performance metrics reflect the system's ability to correctly identify both malignant and benign nodules with high precision. Sensitivity refers to the model's effectiveness in detecting malignant nodules, while specificity indicates its accuracy in classifying benign cases. The combined metrics demonstrate that the system can accurately and efficiently differentiate between the two, providing a robust diagnostic tool for early lung cancer detection.

AI in Lung Cancer Diagnosis

AI's role in revolutionizing medical diagnostics is well-evidenced in this study. The use of deep learning, in particular, allows the system to autonomously learn complex patterns from the CT images, significantly improving diagnostic accuracy. The integration of LDA and MGSA further enhances the model by ensuring dimensional efficiency and optimal classification performance. AI frameworks like this offer clinicians a precise and reliable tool for detecting lung cancer early, potentially saving lives by enabling timely interventions.

Impact and Future Prospects

This AI-driven solution is poised to transform lung cancer screening and diagnostics. The potential benefits include reduced false positives and false negatives, allowing for faster and more accurate diagnoses. Additionally, automating the process reduces the burden on radiologists and pathologists, enabling them to focus on more complex cases while AI handles initial screenings.

Looking ahead, this model could be expanded by incorporating larger and more diverse datasets to further improve its generalizability across different populations. Integrating additional diagnostic imaging modalities, such as PET scans, could also enhance its effectiveness. The continued refinement of AI-based diagnostic systems holds great promise for improving outcomes in lung cancer treatment and beyond.

Conclusion

The study's AI-powered ODNN-LDA framework represents a significant step forward in lung cancer diagnostics. Its high sensitivity, specificity, and overall accuracy highlight the potential of deep learning to revolutionize early cancer detection. By leveraging AI for more precise and efficient diagnosis, healthcare professionals can intervene earlier, ultimately improving patient survival rates. The future of lung cancer diagnosis lies in integrating advanced AI techniques like this, offering new hope in the fight against one of the world's deadliest diseases.