Detection of Lung Cancer Using Artificial Neural Network

Abstract

Detection of lung cancer and colon cancer in histopathology images using ResNet is very important for early and accurate diagnosis, which is a prerequisite for effective treatment. Recent advances in deep learning, especially CNNs, have demonstrated excellent performance in analysing medical images, thus providing a better strategy for detecting blood-eating tumours. Preprocessing of histopathological images to improve the ability to identify malignant areas is dependent on the plan. Then video extraction and image classification are based on ResNet architecture. Histological images of lung and spinal cord tissue annotated with benign and malignant data will be used to train the ResNet model. This will help the model understand what the tissues and cells look like so it can determine where the cancer is in the lung and colon. A comprehensive evaluation will lead to independent data that will verify the accuracy, sensitivity, and specificity of the plan. Comparison with existing methods will demonstrate the robustness and efficiency of the ResNet-based method for diagnosing lung disease and cancer from histopathology images. This study shows how deep learning, specifically ResNet, can help improve doctors' ability to detect lung and breast cancer at an early stage. With timely intervention and treatment planning, this recommendation may improve patient outcomes. But more research and clinical trials are needed to translate this into clinical practice and help develop more effective drugs for cancer and cancer patients. Ongoing research is needed to improve the ResNet-based approach and ensure its effectiveness in real-world situations in lung and cancer treatment.