The objective of this study was to explore the effect of minimally invasive puncture drainage under unsupervised learning algorithm and conservative treatment on the prognosis of patients with cerebral hemorrhage. Fifty patients with cerebral hemorrhage were selected as the research objects. The CT images of patients were segmented by unsupervised learning algorithm, and the application value of unsupervised learning algorithm on CT images of patients with cerebral hemorrhage was evaluated. According to the treatment wishes of the patients themselves and their authorizers, they were divided into 30 patients with cerebral hemorrhage in the minimally invasive group and 20 patients with cerebral hemorrhage in the conservative group. The incidence rate of complications of cerebral hemorrhage, the length of hospitalization of the two groups, hematoma volume at admission, 3 days and 7 days after operation, and the hematoma dissipation rate on the 3rd and 7th day after operation were used as the evaluation index of therapeutic effect. MRS and ADL scores were used as prognostic indicators. The results show that K-means clustering algorithm has high quality and short time for CT image segmentation. The overall incidence rate of complications in minimally invasive group was 10%, lower than that in conservative group (25%) ( P < 0.05 ), and the length of hospitalization in minimally invasive group was longer than that in conservative group ( P < 0.05 ). The hematoma volume of minimally invasive group was 16.5 ± 2.4 mL on the 3rd day after operation, and that of conservative group was 27.4 ± 1.8 mL. There was significant difference between the two groups ( P < 0.05 ). In addition, CT showed that the hematoma reduction degree of minimally invasive group was higher than that of conservative group, and the hematoma dissipation rate was higher than that of conservative group on the 3rd and 7th day ( P < 0.05 ). The good MRS score in minimally invasive group was 3.15 times that in conservative group, and the good ADL score was 1.6 times that in conservative group, and there was significant difference in the total score between the two groups ( P < 0.05 ). Minimally invasive puncture drainage is better than conservative treatment in the clearance of hematoma, which is conducive to the recovery of neurological function and daily life of patients with cerebral hemorrhage and is of great help to the prognosis of patients.

This paper mainly studies the clinical efficacy of sodium nitroprusside and urapidil in the treatment of acute hypertensive intracerebral hemorrhage and analyzes the brain CT image detection based on a deep learning algorithm. A total of 132 cases of acute hypertension admitted to XXX hospital from XX 2019 to XX 2020 were retrospectively analyzed. The diseases of all patients were clinically confirmed, and patients were divided into groups according to the differences in treatment methods. Urapidil was used for group 1; sodium nitroprusside was used for group 2; and urapidil combined with sodium nitroprusside was used for group 3. A convolutional neural network in deep learning is used to construct intelligent processing to classify brain CT images of patients. The network performance of AlexNet, GoogLeNet, and CNN3 is predicted. The results show that GoogLeNet has the highest prediction accuracy of 0.83, followed by AlexNet with 0.80 and CNN3 with 0.74. The results of the performance parameter curve show that the GoogLeNet has the highest performance parameter of 0.89, followed by AlexNet and CNN3 network. The performance parameter curve of machine learning is above 0.80. After five weeks of drug treatment, the hematoma volume was (3.8 ± 2.6) mL in group1, (7.6 ± 2.8) mL in group 2, and (2.8 ± 1.5) mL in group 3. After 5 days of treatment, the patients’ heart rate changed compared with before treatment. Compared with group 2, there were significant differences between groups 1 and 3 ( P < 0.01 ), indicating that the therapeutic effect of the combination group was significantly better than that of the other groups alone. In summary, the combination of sodium nitroprusside and urapidil has a significantly better effect than that of urapidil alone. A convolutional neural network based on deep learning improves the recognition accuracy of medical images.

Cerebral haemorrhage is a serious subtype of stroke, with most patients experiencing short-term haematoma enlargement leading to worsening neurological symptoms and death. The main hemostatic agents currently used for cerebral haemorrhage are antifibrinolytics and recombinant coagulation factor VIIa. However, there is no clinical evidence that patients with cerebral haemorrhage can benefit from hemostatic treatment. We provide an overview of the mechanisms of haematoma expansion in cerebral haemorrhage and the progress of research on commonly used hemostatic drugs. To improve the semantic segmentation accuracy of cerebral haemorrhage, a segmentation method based on RGB-D images is proposed. Firstly, the parallax map was obtained based on a semiglobal stereo matching algorithm and fused with RGB images to form a four-channel RGB-D image to build a sample library. Secondly, the networks were trained with 2 different learning rate adjustment strategies for 2 different structures of convolutional neural networks. Finally, the trained networks were tested and compared for analysis. The 146 head CT images from the Chinese intracranial haemorrhage image database were divided into a training set and a test set using the random number table method. The validation set was divided into four methods: manual segmentation, algorithmic segmentation, the exact Tada formula, and the traditional Tada formula to measure the haematoma volume. The manual segmentation was used as the “gold standard,” and the other three algorithms were tested for consistency. The results showed that the algorithmic segmentation had the lowest percentage error of 15.54 (8.41, 23.18) % compared to the Tada formula method.