Deep Lung: Enhancing Lung Cancer Detection with CNN and Transfer Learning

The days comes to medical picture recognition, com bination of convolutional neural networks and transfer learning are now the good standards. The mistake in recreating the CT image, the doctors efforts to predict lung tumors based on CT image observations are proving to be unsuccessful. Convolutional neural network and transfer learning are a foundational archi tectures for deep learning tasks involving CT images. A useful technique for applying a Convolutional Neural Network that has already been trained to similar tasks is transfer learning. By leveraging the strengths of convolutional neural network, transfer learning extends their applicability, making model more accessible, efficient and the processing time is low. For this study, utilizes a large dataset of more than 20,000 images to explore the task of identifying pulmonary illnesses from CT images. The transfer learning model exhibits robust capabilities in distinguishing between malignant and non-malignant lung tis-sues. The outcome suggest that, Deep Lung Guard holds promise as a potential solution for automated lung tissue classification, with competitive accuracy rates.