Classification of Intracranial Hemorrhage (CT) Images Using CNN- LSTM Method and Image-Based GLCM Features

Abstract

In this study, a hybrid approach is utilized, which combines image-based grey-level co-occurrence matrix attributes and transformed-based attributes. The combination of attribute-based techniques is more accurate in categorizing cerebral hemorrhages in CT images when compared with the transformed attribute-based and image-attribute-based approaches. Deep learning methods, particularly long short-term memory (LSTM), have gained popularity in natural language processing applications like text and sentiment analysis. This paper presents an in-depth deep-learning system that is fully automated. The purpose of the system is to organize radiological data and identify cases of intracranial hemorrhage (ICH) for diagnosis. The structure of the proposed DL model includes 1D convolution neural networks (CNN), long short-term memory (LSTM) units, and a logistic function. A significant dataset consisting of 12,852 radiological reports of head computed tomography (CT) was used to train and assess these components.