Multimodal lung tumor image recognition algorithm based on integrated convolutional neural network

Summary Lung cancer has become one of the major diseases that seriously threaten human health. Early diagnosis of lung cancer is very important for the quality of lung cancer patients. Researching computer‐assisted systems for lung cancer recognition is one of the effective means to help physicians quickly diagnose lung cancer. Therefore, this paper studies the multimodal recognition algorithm for lung images. This algorithm uses CT images, PET images, and PET/CT images of lung tumors as the experimental objects and uses convolutional neural network to realize lung tumor recognition. Based on the recognition of lung tumors by convolutional neural networks, an integrated convolutional neural network was used to identify lung tumors to improve the recognition accuracy and reduce the training time. The experimental results show that the convolutional neural network can effectively identify the lung tumor images. The number of iterations and batch size in the training process will have an impact on the recognition of lung tumors. The integrated convolutional neural network designed is superior to single convolutional neural network in terms of recognition accuracy and time consumption.