Optimized Local Weber and Gradient Pattern-based medical image retrieval and optimized Convolutional Neural Network-based classification. (September 2021) - Bhanu Mahesh, Dhupam; Satyanarayana Murty, Gorti; Rajya Lakshmi, D.
Highlights: The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. Develops a novel OLWGP descriptor for the data retrieval and classification. Generates a novel heuristic algorithm namely J-BMO by integrating JA and the BMO. Uses the novel J-BMO algorithm for selecting the optimal feature points. Uses the proposed OLWGP features to retrieve the suitable images from the database. Develops an optimized CNN for the medical data classification. Abstract: This paper presents a novel image retrieval and classification model for medical images so-called as Content-Based Medical Image Retrieval. For the image retrieval, the main contribution of this paper is to develop a pattern descriptor termed as Optimized Local Weber and Gradient Pattern. A hybrid algorithm with the integration of two well-known meta -heuristic algorithms like Barnacles Mating Optimization and Jaya Algorithm, namely Jaya-based Barnacle Mating Optimization, is used for improving the Optimized Local Weber and Gradient Pattern-based image retrieval. Once the Optimized Local Weber and Gradient Pattern features are determined for training and testing images, the retrieval of images is performed by the logarithmic similarity computation. Moreover, an improved deep learning model called Optimized Convolutional Neural Network is employed for performing the image classification. As an improvement, the activation function of Convolutional Neural Network with sigmoid, tanh, Relu, and RRelu and the maximum epoch are optimized by the same Jaya-based Barnacle Mating Optimization. Finally, extensive experiments are carried out over publicly available datasets, demonstrating that the proposed retrieval and classification models are better than the existing models.