Classification of Images Related to Kidney Cancer using Hybrid Deep Learning

Kidney cancer is a very critical condition that require prompt diagnosis to successful treatment. In this study, it is proposed an enhanced hybrid deep learning (DL) model for kidney cancer image categorization. A research work is carried out in which it advances medical image analysis and shows the way Machine Learning (ML) can assist with kidney-related cancer detection. The study proposes a hybrid model to classify the images related to Kidney disease, where initially the images are pre-processed by Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Then the images are enhanced using Gaussian Mixture based histogram equalization followed by images’ feature extraction using Dense Convolutional neural network. To improve classification precision, the study combines the IAO method with ResNet101. The suggested model beats current models with an amazing precision of 98.90% when assessed using a variety of effectiveness metrics.