Document Type
Article
Source of Publication
Wireless Networks
Publication Date
11-24-2023
Abstract
Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. Computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary for achieving accurate outcomes. Image contrast is one of the most essential visual parameters. Insufficient contrast may present many challenges for computer vision techniques. Traditional contrast adjustment techniques may not be adequate for many applications. Occasionally, these technologies create photos that lack crucial information. The primary contribution of this work is designing a Big Data Architecture (BDA) to improve the dependability of medical systems by producing real-time warnings and making precise forecasts about patient health conditions. A BDA-based Bio-Medical Image Classification (BDA-BMIC) system is designed to detect the illness of patients using Metaheuristic Optimization (Genetic Algorithm) and Gradient Approximation to improve the biomedical image classification process. Extensive tests are conducted on publicly accessible datasets to demonstrate that the suggested retrieval and categorization methods are superior to the current methods. The suggested BDA-BMIC system has average detection accuracy of 94.6% and a sensitivity of 97.3% in the simulation analysis.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
First Page
1
Last Page
22
Disciplines
Computer Sciences
Keywords
Biomedical image classification, Metaheuristic optimization, Gradient approximation, Big data architecture
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Almutairi, Laila; Abugabah, Ahed; Alhumyani, Hesham; and Mohamed, Ahmed A., "Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation" (2023). All Works. 6179.
https://zuscholars.zu.ac.ae/works/6179
Indexed in Scopus
no
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series