Early Lung Cancer Detection Using a Hybrid Model

This paper addresses the global health concern of early detection for increased treatment effectiveness, this research proposes a novel automated technique for lung cancer detection that combines both a Learning Quadratic neural network models (LQNNs) and Convolutional Neural Networks (CNNs). The project aims to create a diverse dataset with images of malignant and non-cancerous lungs carefully divided into training, validation, and test sets. Zooming, shearing, flipping, and normalization are methods of augmentation that enhance the model's ability to recognize important characteristics in the mixed open dataset. To prevent overfitting, the CNN architecture, which was constructed using Keras, includes convolutional, maxpooling, and dropout layers to identify intricate patterns in lung images, linear quadratic, linear quadratic-L, and universal-survival-curve models. Using AI to Predict Lung Cancer Early and Promote Good Health and Well-Being via Proactive Diagnosis. Early halting and model checkpointing procedures are employed during training to ensure optimal performance and generalization.