ArticleComputational intelligence and neuroscience2022
Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma.BMC gastroenterology · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture.Plant methods · 2024Article
- Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.Diagnostics (Basel, Switzerland) · 2023Article
- Medical Image Classifications for 6G IoT-Enabled Smart Health Systems.Diagnostics (Basel, Switzerland) · 2023Article
- Image Classification of Wheat Rust Based on Ensemble Learning.Sensors (Basel, Switzerland) · 2022Article
- Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things.Computational intelligence and neuroscience · 2022Article
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4 authors.
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Abstract
Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset-both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.
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