A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction

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
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Abstract

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.
一种新的基于深度学习的黄蜂优化方法,用于增强脑肿瘤检测和物理治疗预测
脑肿瘤是最致命的疾病之一,其特征是大脑中突触的异常生长。早期发现可以提高脑肿瘤的诊断率,准确的诊断是有效治疗的关键。研究人员开发了几种深度学习分类方法来诊断脑肿瘤。此外,这些类型的肿瘤会严重损害身体活动,表现出广泛的症状。因此,每个病人都需要一个个性化的物理治疗计划,以满足他们的具体需求。然而,仍然存在一些挑战,包括需要有能力的专家使用深度学习模型对脑肿瘤进行分类,以及创建最准确的脑肿瘤分类深度学习模型的挑战。为了应对这些挑战,我们提出了一种基于先进的元启发式算法和深度学习的高度准确和高效的方法。为了识别不同类型的儿童脑肿瘤,我们专门开发了一个最佳残差学习架构。我们还提出了蜘蛛黄蜂优化(SWO)算法,该算法旨在通过特征选择来提高性能。该算法通过平衡收敛速度和解的多样性来提高优化的有效性。我们首先将算法从连续转换为二值,将其与k近邻(KNN)算法相结合进行分类,并在阿卜杜拉国王医院采集的脑MRI图像数据集上对其进行评估。我们的分析显示,在准确性、灵敏度、特异性和f1评分等指标方面,它优于其他传统算法。我们通过使用该模型选择从Resnet50V2模型中提取的最优特征用于儿童脑肿瘤检测,证明了该模型的整体有效性。我们将提出的SWO+KNN模型与其他深度学习架构(如MobileNetV2、Resnet50V2)和机器学习算法(如KNN、支持向量机SVM和随机森林(RF))进行了比较。实验结果表明,所提出的SWO+KNN模型优于其他成熟的深度学习模型和先前的研究。SWO+KNN在二元分类和多类分类上的准确率分别为97.5%和95.5%。结果清楚地证明了所提出的SWO+KNN模型能够准确地对脑肿瘤进行分类。
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CiteScore
5.90
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