Lung Cancer Classification using ResNet50

Lung cancer is still the most often occurring and lethal form of cancer globally, contributing significantly to the annual death from cancer. With the use of efficient and automated testing technologies, the project seeks to lower mortality rates and improve early detection accuracy. Improving survival rates requires early diagnosis, but because current diagnostic techniques such as imaging and biopsies rely so much on clinical presentation, they frequently lead to late diagnoses. One of the most efficient deep learning architectures for applications involving image recognition is Convolutional Neural Networks. A version of the ResNet (Residual Networks) architecture known as ResNet50 has become well-known for its capacity to lessen the vanishing gradient issue and make intense network training possible. ResNet50, which has been pre-trained on ImageNet, is used in this study as a feature extractor to detect lung cancer. The outcomes of the experiments indicate that the ResNet50-based model is highly accurate in differentiating between malignant lung tissues and those that are not. In addition to Accuracy, the model's efficiency in managing and analyzing images offers significant advantages over traditional diagnostic methods. The application of ResNet50 to the diagnosis of lung cancer demonstrates the Deep learning transformative potential models in the field of cancer diagnostics. This study provides a reliable, accurate, and valuable diagnostic tool, which contributes to the ongoing efforts to improve the early identification and treatment outcomes for patients with lung cancer. Lung tumor identification and diagnosis have greatly benefited from the use of deep learning and convolutional neural networks. These technologies improve the precision and effectiveness of medical imaging analysis by utilizing advanced image processing and pattern recognition capabilities.