PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

Abstract

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.