A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction
A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to...
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Elsevier
2025-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990025000175 |
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| author | Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah |
| author_facet | Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah |
| author_sort | Suleiman Daoud |
| collection | DOAJ |
| description | A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms such as KNN, Support Vector Machine SVM, and Random Forest (RF). The experimental results indicate that the proposed SWO+KNN model outperforms other well-established deep learning models and previous studies. SWO+KNN achieved accuracy rates of 97.5 % and 95.5 % for both binary classification and multiclass classification, respectively. The results clearly demonstrate the ability of the proposed SWO+KNN model to accurately classify brain tumors. |
| format | Article |
| id | doaj-art-8f74fe556f0342c19da4e534947dac1e |
| institution | DOAJ |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-8f74fe556f0342c19da4e534947dac1e2025-08-20T03:07:50ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-01710019310.1016/j.cmpbup.2025.100193A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy predictionSuleiman Daoud0Ahmad Nasayreh1Khalid M.O. Nahar2Wlla k. Abedalaziz3Salem M. Alayasreh4Hasan Gharaibeh5Ayah Bashkami6Amer Jaradat7Sultan Jarrar8Hammam Al-Hawamdeh9Absalom E. Ezugwu10Raed Abu Zitar11Aseel Smerat12Vaclav Snasel13Laith Abualigah14Neurosurgery Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, JordanArtificial Intelligence and Data Innovation Office, King Hussein Cancer Center, Amman, JordanDepartment of Information Technology and Computer Sciences, Yarmouk University, Irbid 211633, JordanDepartment of Rehabilitation science, Faculty of applied medical science, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, JordanNeurosurgery Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, JordanArtificial Intelligence and Data Innovation Office, King Hussein Cancer Center, Amman, JordanDepartment of Medical Laboratory Sciences, Al Balqa Applied University, Salt, JordanNeurosurgery Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, JordanNeurosurgery Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, JordanNeurosurgery Department, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, JordanUnit for Data Science and Computing, North-West University, 11 Hofman Street, Potchefstroom 2520, South Africa; Corresponding authors.College of Engineering and Computing, Liwa University, Abu Dhabi, United Arab EmiratesFaculty of Educational Sciences, Al-Ahliyya Amman University, Amman 19328, Jordan; Computer Technologies Engineering, Mazaya University College, Nasiriyah, IraqFaculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 70800 Poruba-Ostrava, Czech RepublicComputer Science Department, Al al-Bayt University, Mafraq 25113, Jordan; Corresponding authors.A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms such as KNN, Support Vector Machine SVM, and Random Forest (RF). The experimental results indicate that the proposed SWO+KNN model outperforms other well-established deep learning models and previous studies. SWO+KNN achieved accuracy rates of 97.5 % and 95.5 % for both binary classification and multiclass classification, respectively. The results clearly demonstrate the ability of the proposed SWO+KNN model to accurately classify brain tumors.http://www.sciencedirect.com/science/article/pii/S2666990025000175Brain tumorPediatricMachine learningRehabilitationPhysical therapyDeep learning |
| spellingShingle | Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction Computer Methods and Programs in Biomedicine Update Brain tumor Pediatric Machine learning Rehabilitation Physical therapy Deep learning |
| title | A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| title_full | A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| title_fullStr | A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| title_full_unstemmed | A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| title_short | A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| title_sort | novel deep learning based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction |
| topic | Brain tumor Pediatric Machine learning Rehabilitation Physical therapy Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000175 |
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