Adamu Mohammed Jajere, Kawuwa Halima Bello, Qiang Li, Nyatega Charles Okanda, Younis Ayesha, Fahad Muhammad, Dauya Salisu Samaila
Department of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, China.
Department of Computer Science, Yobe State University, Damaturu 600213, Nigeria.
Brain Sci. 2024 Nov 25;14(12):1178. doi: 10.3390/brainsci14121178.
BACKGROUND/OBJECTIVES: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.
This study proposes a hybrid model that integrates MobileNetV2 for feature extraction with a Support Vector Machine (SVM) classifier for the classification of brain tumors. The model was trained and validated using the Kaggle MRI brain tumor dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, and no tumor. MobileNetV2's efficient architecture was leveraged for feature extraction, and SVM was used to enhance classification accuracy.
The proposed hybrid model showed excellent results, achieving Area Under the Curve (AUC) scores of 0.99 for glioma, 0.97 for meningioma, and 1.0 for both pituitary tumors and the no tumor class. These findings highlight that the MobileNetV2-SVM hybrid not only improves classification accuracy but also reduces computational overhead, making it suitable for broader clinical use.
The MobileNetV2-SVM hybrid model demonstrates substantial potential for enhancing brain tumor diagnostics by offering a balance of precision and computational efficiency. Its ability to maintain high accuracy while operating efficiently could lead to better outcomes in medical practice, particularly in resource limited settings.
背景/目的：磁共振成像（MRI）在脑肿瘤诊断中起着至关重要的作用，它能够在不使用电离辐射的情况下清晰显示软组织。鉴于脑肿瘤发病率不断上升，迫切需要可靠的诊断工具，因为误诊可能导致有害的治疗决策和不良后果。虽然机器学习在医学诊断方面取得了显著进展，但要同时实现高精度和计算效率仍然是一项关键挑战。
本研究提出了一种混合模型，该模型将用于特征提取的MobileNetV2与用于脑肿瘤分类的支持向量机（SVM）分类器相结合。使用Kaggle MRI脑肿瘤数据集对该模型进行训练和验证，该数据集包含7023张图像，分为四种类型：神经胶质瘤、脑膜瘤、垂体瘤和无肿瘤。利用MobileNetV2的高效架构进行特征提取，并使用SVM提高分类准确率。
所提出的混合模型显示出优异的结果，神经胶质瘤的曲线下面积（AUC）得分为0.99，脑膜瘤为0.97，垂体瘤和无肿瘤类均为1.0。这些发现表明，MobileNetV2-SVM混合模型不仅提高了分类准确率，还减少了计算开销，使其适用于更广泛的临床应用。
MobileNetV2-SVM混合模型通过在精度和计算效率之间取得平衡，在增强脑肿瘤诊断方面显示出巨大潜力。它在高效运行的同时保持高精度的能力可能会在医疗实践中带来更好的结果，特别是在资源有限的环境中。
