An efficient image descriptor for image classification and CBIR

Abstract

Pattern recognition and feature extraction of images always have been important subjects in improving the performance of image classification and Content-Based Image Retrieval (CBIR). Recently, Machine Learning and Deep Learning algorithms are utilized widely in order to achieve these targets. In this research, an efficient method for image description is proposed which is developed by Machine Learning and Deep Learning algorithms. This method is created using combination of an improved AlexNet Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors. Furthermore, the Principle Component Analysis (PCA) algorithm has been used for dimension reduction. The experimental results demonstrate the superiority of the offered method compared to existing methods by improving the accuracy, mean Average Precision (mAP) and decreasing the complex computation. The experiments have been run on Corel-1000, OT and FP datasets.

Diagnosing COVID-19 disease using an efficient CAD system

“…Today, many researchers are trying to develop methods for diagnosing COVID-19. One of these effective ways is proposing CAD systems based on convolutional neural networks (CNNs) [28] , [7] . In the following, some recent methods which have been proposed for achieving this target are described.…”

“…Although state-of-the-arts networks such as EfficentNet, MobileNet, and ResNet have been very successful in different computer vision applications [26] , the results of researches [27] , [5] demonstrate that AlexNet CNN still has its capability. In research [5] , a modified AlexNet CNN and also a hybrid AlexNet CNN have been proposed for diagnosing COVID-19 using chest X-ray images.…”

“…For diagnosing many diseases and helping physicians, computer-aided diagnosis (CAD) systems have been considered as an effective approach up to now [19] , [26] , [28] . Hence, in this study, a computer-aided diagnosis system called COV-CAD is proposed.…”

“…The proposed feature extractor is created by inspiring of AlexNet Convolutional Neural Network (CNN) and it is using the LeakyReLU activation function for increasing efficiency instead of ReLU [9] . In addition, in order to reduce parameters, a fully connected (FC) layer with dimensions 1 × 512 is used instead of the 20 th layer of AlexNet CNN that is an FC layer with dimensions is 1 × 4096 [28] . Another FC layer with 1 × 64 dimensions is added at the end of the proposed feature extractor as a description of each input image for creating the feature vector.…”

“…Because of the ability of CBIR systems in showing the most similar images to a query image (the image of a patient), the proposed method uses this approach to find the most similar cases [27] , [30] . The ability of the proposed COV-CAD system in finding and retrieving the most similar subjects could help physicians to conduct further statistical evaluations on groups of similar patients’ profiles.…”