Automated Hybrid Model-Based Lung Cancer Prediction Using Histopathology Images with Deep Learning

Abstract: Lung cancer is a major cause of death around the world. Finding it early can help save lives. Doctors usually look at tissue images under a microscope to find lung cancer, but this takes time and can sometimes lead to mistakes. In this work, we created a computer system that uses deep learning to help detect lung cancer automatically. We use special AI models called GANs to make the images clearer and create more samples for training. Our system looks at three types of lung tissue: normal, adenocarcinoma, and squamous cell carcinoma. To improve results, we combine three deep learning models—EfficientNet, ResNet, and DenseNetthat work together to make better decisions. We trained the system using both real and AI-created images. The results showed higher accuracy and better performance than using just one model. We also added a language model to help doctors with decision-making and used tools to monitor the system in real time. The system is easy to set up and scale using Docker and Kubernetes. Our goal is to help doctors diagnose lung cancer faster and more accurately. In the future, we plan to use more data, improve how the model explains its results, and keep patient data safe using privacy-friendly methods.