Ali Amin Saad, Alqudah Mashal Kasem Sulieman, Ateeq Almutairi Saleh, Almajed Rasha, Rustom Al Nasar Mohammad, Ali Alkhazaleh Hamzah
College of Engineering and IT, University of Dubai, Academic City, 14143, Dubai, United Arab Emirates.
Digital Transformation & Information Programs Department, Institute of Public Administration, Riyadh, Saudi Arabia.
Heliyon. 2024 Jul 3;10(14):e34050. doi: 10.1016/j.heliyon.2024.e34050. eCollection 2024 Jul 30.
This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed. Experiments were conducted using the "Whole Brain Atlas (WBA)" database, which contains annotated MRI images. The results showed superior efficiency in accurately detecting brain tumors from MRI images, demonstrating the potential of the method in enhancing accuracy and efficiency. The proposed method utilizes hierarchical methodology, preprocessing techniques, and optimization of the Extreme Learning Machine with the Modified Sailfish optimizer to improve accuracy rates and decrease the time needed for brain tumor diagnosis. The proposed method outperformed other methods in terms of accuracy, recall, specificity, precision, and F1 score in medical imaging diagnosis. It achieved the highest accuracy at 93.95 %, with End/End and CNN attaining high values of 89.24 % and 93.17 %, respectively. The method also achieved a perfect score of 100 % in recall, 91.38 % in specificity, and 75.64 % in F1 score. However, it is crucial to consider factors like computational complexity, dataset characteristics, and generalizability before evaluating the effectiveness of the method in medical imaging diagnosis. This approach has the potential to make substantial contributions to medical imaging and aid healthcare professionals in making prompt and precise treatment decisions for brain tumors.
本研究提出了一种用于在磁共振成像（MRI）中检测脑肿瘤的分层自动化方法，重点是对图像进行预处理以提高质量并消除伪影或噪声。然后使用改进的极限学习机来诊断脑肿瘤，并将其与改进的旗鱼优化器集成以提高其性能。改进的旗鱼优化器是一种元启发式算法，以有效探索优化空间和提高收敛速度而闻名。使用包含带注释的MRI图像的“全脑图谱（WBA）”数据库进行了实验。结果表明，该方法在从MRI图像中准确检测脑肿瘤方面具有卓越的效率，证明了该方法在提高准确性和效率方面的潜力。所提出的方法利用分层方法、预处理技术以及使用改进的旗鱼优化器对极限学习机进行优化，以提高准确率并减少脑肿瘤诊断所需的时间。在医学成像诊断中，所提出的方法在准确率、召回率、特异性、精确率和F1分数方面优于其他方法。它达到了最高准确率93.95%，而End/End和CNN分别达到了89.24%和93.17%的高值。该方法在召回率方面也达到了100%的满分，特异性为91.38%，F1分数为75.64%。然而，在评估该方法在医学成像诊断中的有效性之前，考虑计算复杂性、数据集特征和通用性等因素至关重要。这种方法有可能为医学成像做出重大贡献，并帮助医疗保健专业人员为脑肿瘤做出迅速而精确的治疗决策。
