Performance evaluation of deep learning techniques for lung cancer prediction
B S Deepapriya1, Parasuraman Kumar2, G Nandakumar3
1Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Erode, Tamilnadu India.
Soft Computing
|May 31, 2023
Summary
This study explores deep learning models for early lung disease prediction using medical imaging. It identifies the most effective deep learning techniques for accurate lung disease detection.
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Healthcare
- Pulmonology
Background:
- Rising global pollution levels correlate with increased lung disease mortality.
- Early disease detection is crucial for effective patient outcomes.
- Computer-Aided Diagnosis (CAD) systems enhance medical diagnostics through automation.
Purpose of the Study:
- To evaluate and identify the best-performing deep learning techniques for lung disease prediction.
- To leverage artificial intelligence for earlier and more accurate lung disease diagnosis.
- To compare various deep learning models using chest X-ray and CT scan inputs.
Main Methods:
- Experimentation with several deep learning models.
- Utilizing chest X-ray and CT scan images as input data.
- Performance evaluation using metrics like precision, recall, accuracy, and Jaccard index.
Main Results:
- Identification of superior deep learning models for lung disease detection.
- Quantification of model performance using established metrics.
- Demonstration of deep learning's efficacy in computer-aided lung disease diagnosis.
Conclusions:
- Deep learning models show significant promise for early lung disease prediction.
- The study provides insights into optimal AI techniques for medical imaging-based diagnostics.
- This research supports the integration of automated systems in clinical practice for improved patient care.
