Generative Deep Belief Model for Improved Medical Image Segmentation

Abstract

Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform (QCWT). Then, feature extraction and selection are done after the pre-processing of the image. The features are optimally selected by the Golden Eagle Optimization (GEO) method. Specifically, the processed image is segmented accurately using the proposed Generative Heap Belief Network (GHBN) technique. The execution of this research is done on MATLAB software. According to the results of the experiments, the proposed framework is superior to current techniques in terms of segmentation performance with a valid accuracy of 99%, which is comparable to the other methods.