Harnessing Continual Learning in Deep Neural Networks for Improved Lung Cancer Diagnosis

Deep learning methods have shown tremendous potential in the prompt and accurate diagnosis of lung cancer by allowing the discovery of minute patterns in large-scale medical data. To predict and diagnose lung cancer, the research gives a thorough review of the use of continuous learning approaches. Utilizing a framework for constant learning, deep neural networks are taught to continually adapt to and learn from new data, improving their predicting skills over time. To gain a more thorough picture of the illness course, the research focuses on integrating longitudinal patient data, including several imaging modalities, genetic profiles, and clinical records. Due to the model's constant updating of its information and prevention of catastrophic forgetting, a more dependable and accurate prediction of lung cancer is made possible by the continual learning technique. Additionally, the approach handles the issue of data imbalance and small sample sizes frequently present in medical datasets, ensuring reliable predictions even in the absence of such data. Experimental results reveal that the continual learning methodology outperforms conventional deep learning techniques, highlighting its potential to improve lung cancer diagnostic and treatment accuracy and efficacy dramatically.