¬A Decision Support System for Kidney Cancer Detection in Renal Images Using Deep Learning Models
Keywords:
Computational intelligence; Nature-inspired algorithm; Deep learning; Decision support system; Kidney cancerAbstract
Kidney cancer comes in various kinds where. Renal Cell Carcinoma (RCC) is the most severe as well as common sort of cancer, which is accountable for around 85% of adults. The earlier diagnosis of kidney cancer has enormous advantages in producing preventive measures that reduce the effect, reduce death rates, and overcome the tumor. Manually detecting whole slide images (WSI) of renal tissues is a basic RCC prognosis and diagnosis device. However, manual analysis of RCC is disposed to inter-subject variability and is time-consuming. Compared to the time-consuming and tedious classical diagnoses, the automatic detection algorithm of deep learning (DL) could improve test accuracy, save diagnoses time, reduce the radiologist's workload, and reduce costs. The study presents a Computational Intelligence with a Deep Learning Decision Support System for Kidney Cancer (CIDL-DSSKC) technique on renal images. The presented CIDL-DSSKC model observes the renal imageries for identifying and recognizing kidney cancer. The presented CIDL-DSSKC method uses Median and Wiener filters for image preprocessing. The CIDL-DSSKC technique uses the Xception model to derive a useful set of feature vectors. Besides, the flower pollination algorithm (FPA) is employed to choose parameters linked to the Xception method optimally. For the identification and classification of kidney cancer, the ????-variational autoencoder (????-VAE) approach is employed. A renal image dataset containing many images has been used in the experimental outcomes of the CIDL-DSSKC method.
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