An Enhanced Lung Cancer Detection Using Conventional Neural Network

Abstract

The fact that lung cancer is still one of the most common causes of cancer-related deaths globally is mostly because of late- stage diagnosis and few available treatment choices. While early detection is essential for bettering patient outcomes, conventional screening techniques frequently don't have the sensitivity and specificity needed. In this work, we suggest a machine learning-based method for utilizing imaging data to identify lung cancer. We trained and assessed several machine learning models on a dataset that included [explain your dataset, including size and attributes]. Preprocessing methods such as were used to improve the data's quality. Several machine learning methods, including [all the algorithms used], were utilized to create prediction models for the identification of lung cancer. Our best- performing model achieves [name important performance metrics, such as accuracy, sensitivity, and specificity], which shows encouraging performance. To further confirm the efficacy of our strategy, we contrasted the performance of our machine learning models with [state comparison with current techniques or benchmarks].
By utilizing machine learning, this work adds to the expanding corpus of research on lung cancer diagnosis. Our results demonstrate how machine learning algorithms can help physicians identify patients earlier, which will improve patient outcomes and lower the death rate from lung cancer