Ischemic stroke is one of the most deadly illnesses in the world, leading to high mortality. Due to lung disease, stroke is the abnormal growth of cells characterized by a single irregular cell and spreads throughout the body. Therefore, to detect and heal the affected area at an early stage, it is necessary to detect the affected area after application. Ischemic stroke is generally regarded as an essential indicator of stroke rehabilitation care. The previous method uses SVM (Support Vector Machine) and STFT (Short Time Fourier Transform Algorithm) to process an image processing system based on stroke detection. This is more accurate and efficient for CT (Computed Tomography) images. The conversion method is significantly slower, and the advanced risk architecture cannot verify the image. The proposed FPGA (Field Programmable Gate Array) and CNN (Convolutional Neural Network) are used to develop image processing and easily interact with the database without introducing complexity. FPGA (Field Programmable Gate Array) is mainly realized by ASIC (Application Specific Integrated Circuit). The system speeds up detecting strokes and lung diseases and can be used as a single process system or another biomedical imaging system component. According to the medical big data system, the image processing system relies on bilateral filtering, edge detection, multiple thresholds, image segmentation, morphological image processing, and image labeling to collect stroke symptoms.

缺血性中风是世界上最致命的疾病之一，导致高死亡率。由于肺部疾病，中风是以单个不规则细胞为特征的细胞异常生长，并扩散到全身。因此，为了及早发现并治愈患处，有必要在施用后检测患处。缺血性中风通常被视为中风康复护理的重要指标。先前的方法使用SVM（支持向量机）和STFT（短时傅立叶变换算法）来处理基于笔画检测的图像处理系统。对于CT（计算机断层扫描）图像，这更加准确有效。转换方法要慢得多，并且高级风险体系结构无法验证图像。提出的FPGA（现场可编程门阵列）和CNN（卷积神经网络）用于开发图像处理并轻松与数据库进行交互，而不会引起复杂性。FPGA（现场可编程门阵列）主要由ASIC（专用集成电路）实现。该系统加快了中风和肺部疾病的检测速度，可以用作单个过程系统或另一个生物医学成像系统组件。根据医学大数据系统，图像处理系统依靠双边过滤，边缘检测，多个阈值，图像分割，形态图像处理和图像标记来收集中风症状。FPGA（现场可编程门阵列）主要由ASIC（专用集成电路）实现。该系统加快了中风和肺部疾病的检测速度，可以用作单个过程系统或另一个生物医学成像系统组件。根据医疗大数据系统，图像处理系统依赖于双边过滤，边缘检测，多个阈值，图像分割，形态图像处理和图像标记来收集中风症状。FPGA（现场可编程门阵列）主要由ASIC（专用集成电路）实现。该系统加快了中风和肺部疾病的检测速度，可以用作单个过程系统或另一个生物医学成像系统组件。根据医学大数据系统，图像处理系统依靠双边过滤，边缘检测，多个阈值，图像分割，形态图像处理和图像标记来收集中风症状。