Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification

Abstract

Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet).