Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Xu, Wenting; Tang, Weizhou; Wu, Liangqun; Jiang, Qianzhu; Tian, Qiyuan; Wang, Ce; Lu, Lina; Kong, Ying

doi:10.1155/2022/5863082

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…It addresses the limitations of neural networks in predicting HE through quantitative volume and texture analysis (CTTA) of CT images [28]. A fuzzy C-means (FCM) intelligent segmentation algorithm was established by Xu et al [29] for intelligent segmentation of patients' brain CT images, which holds high clinical value for the early prediction of HE in patients with ICH.…”

Section: Related Workmentioning

confidence: 99%

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Li,

Du,

et al. 2024

BMC Med Inform Decis Mak

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Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.

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Section: Related Workmentioning

confidence: 99%

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Li,

Du,

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…The Fuzzy C-means (FCM) intelligent segmentation algorithm [129] was developed for early detection of enlarged hematoma in patients with intracerebral hemorrhage (ICH) on CT images. The processing of cranial CT images using the FCM algorithm has high clinical value in predicting early hematoma in ICH patients.…”

Section: A Some Typical Applicationsmentioning

confidence: 99%

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Dao¹,

Ly²

2023

IJACSA

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Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW), and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.

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Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Cited by 2 publications

References 27 publications

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

Hematoma expansion prediction based on SMOTE and XGBoost algorithm

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

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