Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI:10.1007/s11571-025-10259-5
B Nageswara Rao, U Rajendra Acharya, Ru-San Tan, Pratyusa Dash, Manoranjan Mohapatra, Sukanta Sabut
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引用次数: 0

Abstract

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 ("ICH" class) and 1648 ("Normal" class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical Sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings.

基于卷积自编码器的深度学习脑CT图像脑出血分类。
脑出血(ICH)是中风的一种常见形式,影响着全世界数百万人。该病的发病率与高死亡率和发病率有关。使用脑非对比计算机断层扫描(NCCT)进行准确的诊断对于可能挽救生命的手术决策至关重要。获得专家读者的机会有限,观察者之间的差异给及时和准确的脑出血诊断造成了障碍。我们提出了一种使用NCCT图像进行ICH自动诊断的混合深度学习模型,该模型包括卷积自编码器(CAE)和密集神经网络(DNN),前者用于提取降维数据的特征,后者用于分类。为了确保模型能够推广到新的数据,我们使用了十倍交叉验证和保留方法来训练它。系统地实现了基于主成分分析(PCA)的降维和分类。该研究数据集包括来自108名患者的1645(“ICH”类)和1648(“正常”类属于非出血性中风患者)标记图像,这些患者在2020年至2023年期间在Kalinga医学科学研究所的64层计算机断层扫描仪上接受了CT检查。我们开发的CAE-DNN混合模型准确率为99.84%,灵敏度为99.69%,特异性为100%,精密度为100%,f1评分为99.84%,优于比较PCA-DNN模型以及文献中已发表的结果。此外,使用显著性图,我们的CAE-DNN模型可以突出显示图像上与ICH区域密切相关的区域,这些区域已由专家读者手动绘制。CAE-DNN模型证明了准确的脑出血检测和定位的概念验证,这有可能在临床环境中实现使用NCCT图像优先治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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