Deep Learning-Based Lung Cancer Classification: Recent Developments and Future Prospects
Almas Begum, Alex David S, D Hemalatha, Lavanya Sita Sai Kollipara
Abstract
This article discusses about potential of deep learning in lung cancer diagnosis using medical imaging, specifically focusing on the performance of various deep learning models in accurately identifying lung nodules from CT scans. Based on the findings, it was revealed that various deep learning algorithms, mainly deals with convolutional neural networks, demonstrate a remarkable degree of accuracy and an appropriate trade-off between sensitivity and specificity in correctly identifying true positives and true negatives. However, there is a need for more detailed analysis and comparison of these methods to fully understand their performance characteristics. The article also identifies several potential future scopes of deep learning in medical imaging and lung cancer diagnosis, including integrating deep learning models into clinical practice, large-scale multi-center studies to validate the effectiveness of deep learning-based approaches, improving the interpretability of deep learning models, integrating information from multiple imaging modalities, and implementing deep learning-based approaches in screening programs to improve early detection and patient outcomes. Overall, the results suggest that deep learning has significant potential to improve lung cancer diagnosis and treatment, but further research is needed to fully realize this potential.