| Autor: |
Mucha Swetha, Ramesh Babu A. |
| Jazyk: |
English<br />French |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
MATEC Web of Conferences, Vol 392, p 01075 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2261-236X |
| DOI: |
10.1051/matecconf/202439201075 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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