Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval

Abstract

Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.

Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection

“…Traditional counterfeit detection systems frequently fail to detect subtle alterations and produce reliable results [5]. Researchers have proposed numerous strategies for uncovering image forgeries [4,[6][7][8]. Traditional forgery detection approaches focus on seeing numerous artifacts inside modified images, such as changes in lighting, contrast, compression, sensor disturbances, and reflections.…”

“…An end-to-end trainable BusterNet-based DL framework for duplicate image fraud prevention and detection was proposed in [5]. Authors in [6] employed DL methods to detect image forgeries in real-world datasets. Their compact model outperformed the baseline methods.…”