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Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage
Comput Math Methods Med. 2022 Jun 14;2022:5863082. doi: 10.1155/2022/5863082. eCollection 2022.ABSTRACTThe aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy C-means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to dif...
Source: Computational and Mathematical Methods in Medicine - June 24, 2022 Category: Statistics Authors: Wenting Xu Weizhou Tang Liangqun Wu Qianzhu Jiang Qiyuan Tian Ce Wang Lina Lu Ying Kong Source Type: research

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration
Comput Math Methods Med. 2022 Apr 22;2022:6204089. doi: 10.1155/2022/6204089. eCollection 2022.ABSTRACTThe aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two patients with intracerebral hemorrhage were divided into experimental group (46 cases, minimally invasive aspiration therapy) and control group (46 cases, traditional craniotomy therapy) according to different treatment methods, and CT image scanning was performed. In addition...
Source: Computational and Mathematical Methods in Medicine - May 2, 2022 Category: Statistics Authors: Junfeng Sun Xiaojun Zheng Qiang Gao Xiaofeng Wang Yu Qiao Jialong Li Source Type: research

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images
In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evide...
Source: Computational and Mathematical Methods in Medicine - April 26, 2022 Category: Statistics Authors: B Nageswara Rao Sudhansu Mohanty Kamal Sen U Rajendra Acharya Kang Hao Cheong Sukanta Sabut Source Type: research