The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model’s sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network’s core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.
脑肿瘤的典型特征是异常脑细胞的快速增殖，这对成年人构成重大健康风险，可能导致器官功能严重受损甚至死亡。这些肿瘤在大小、质地和位置上存在广泛差异。磁共振成像（MRI）是定位恶性肿瘤的关键工具，但手动检测脑肿瘤不仅困难且耗时，还可能导致误差。为此，我们提出一种改进的YOLOv7模型，在优化的脑肿瘤检测系统中实现对脑膜瘤、胶质瘤和垂体瘤的精准识别。通过采用多种滤波器对原始MRI图像进行增强处理，提升了影像的视觉呈现效果。为进一步优化模型训练，我们对公开脑肿瘤数据集实施了数据增强技术。经处理的数据集涵盖多种病例类型，包括2548张胶质瘤图像、2658张垂体瘤图像、2582张脑膜瘤图像及2500张非肿瘤图像。我们在YOLOv7中引入卷积注意力模块（CBAM）机制，显著增强了特征提取能力，使模型能更聚焦于脑部病变的关键区域。通过在网络核心架构中加入快速空间金字塔池化增强层（SPPF+），进一步提升了模型灵敏度。改进后的YOLOv7采用解耦检测头设计，能够从多样化数据中高效提取有效信息。此外，通过引入双向特征金字塔网络（BiFPN）加速多尺度特征融合，优化了肿瘤相关特征的整合能力。实验结果表明，该方法在肿瘤检测整体准确率上优于现有先进模型，展现出作为脑肿瘤诊断领域辅助决策工具的重要应用潜力。
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging
肿瘤(癌症)患者之家