用手机摄像头热成像+MobileNet实现低成本高效脑瘤检测
Brain Tumor Detection through Thermal Imaging and MobileNET
- 基于热成像与MobileNet构建轻量级检测模型
- 平均准确率达98.5%，显著降低计算资源消耗
- 适合资源有限的基层医疗场景快速部署
大脑在调节身体功能和认知过程中起着关键作用，脑瘤对人类健康构成重大威胁。精准及时的检测是有效治疗和改善患者预后的关键。传统检测方法如活检、MRI和CT扫描常因成本高、需专业医疗人员而面临挑战。近年来，机器学习与深度学习在医学图像（尤其是MRI）的脑瘤识别与分类方面展现出强大能力。然而，传统模型存在计算需求高、需大量数据、训练时间长等局限，限制了其可及性与效率。本研究采用MobileNet模型实现脑瘤的高效检测，创新点在于构建一个低资源占用、运行快速且通过图像处理技术提升精度的肿瘤检测系统。该方法平均准确率达到98.5%。
原文摘要 · Abstract (English)
Brain plays a crucial role in regulating body functions and cognitive processes, with brain tumors posing significant risks to human health. Precise and prompt detection is a key factor in proper treatment and better patient outcomes. Traditional methods for detecting brain tumors, that include biopsies, MRI, and CT scans often face challenges due to their high costs and the need for specialized medical expertise. Recent developments in machine learning (ML) and deep learning (DL) has exhibited strong capabilities in automating the identification and categorization of brain tumors from medical images, especially MRI scans. However, these classical ML models have limitations, such as high computational demands, the need for large datasets, and long training times, which hinder their accessibility and efficiency. Our research uses MobileNET model for efficient detection of these tumors. The novelty of this project lies in building an accurate tumor detection model which use less computing re-sources and runs in less time followed by efficient decision making through the use of image processing technique for accurate results. The suggested method attained an average accuracy of 98.5%.
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