Date of Publication :2nd September 2024

Abstract:Lung cancer's significant global burden underscores the need for effective early detection methods, which are often challenged by complex tumor patterns. This research provides an advanced detection method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to overcome these issues. The proposed hybrid architecture integrates deep learning along with image processing and data augmentation techniques to enhance both accuracy and generalization. In this approach, CNNs extract essential spatial information from lung CT scans, while LSTMs collect temporal connections which improves the model's overall performance. The model achieves an impressive 92.4% accuracy, showing major advances in lung cancer detection and the possibility for better patient outcomes through more accurate and reliable diagnostic capabilities.

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