An efficient attention encoder decoder-based residual-UNet for the segmentation of liver and lung tumour

In the world, liver and lung cancer are the two types of cancers that occur in the human body. Liver and lung tumour segmentation is a basic process in treating and diagnosing diseases. The automated detection of these two cancers undergoes stages like dataset collection, pre-processing, and optimisation-based segmentation. Datasets like 3DIRCADb for the liver and LIDC-IDRI for the lung are initially obtained. Then, the hybrid deep learning model with optimisation is carried out for the segmentation process. The deep learning model attention encoder decoder-based residual-UNet is used to segment the liver and extract the region of interest. Similarly, the same process is carried out for lung tumour segmentation. The metaheuristic optimisation fire hawk algorithm is introduced. The segmentation performance of the proposed liver and lung segmentation model is carried out using different measures. On the liver and lung datasets, the proposed approach achieves dice values of 0.901 and 0.916, respectively.