Classification of intracranial hemorrhage (CT) images using CNN-LSTM method and image-based GLCM features

MATEC Web of Conferences Pub Date : 2024-05-04 DOI:10.1051/matecconf/202439201075

Swetha Mucha, A. Ramesh Babu

引用次数: 0

Abstract

A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.

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使用 CNN-LSTM 方法和基于图像的 GLCM 特征对颅内出血（CT）图像进行分类

采用了一种混合方法，将基于特征的转换特征方法与基于图像的灰度共现矩阵特征相结合。在对脑出血 CT 图像进行分类时，基于特征的组合策略比基于图像特征和基于变换特征的技术表现更好。使用深度学习技术，特别是长短期记忆（LSTM）进行自然语言处理，已成为情感分析和文本分析等应用的首选。本研究提出了一种完全自动化的深度学习系统，用于对放射学数据进行分类，以诊断颅内出血（ICH）。长短期记忆（LSTM）单元、逻辑函数和一维卷积神经网络（CNN）构成了所建议的自动深度学习架构。这些组件都是通过一个包含 12,852 份头部计算机断层扫描（CT）放射报告的大型数据集进行训练和评估的。

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

MATEC Web of Conferences

自引率

0.00%

发文量

342

审稿时长

6 weeks

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