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Published on: April 13, 2013
An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks
Manikandan Rajagopal1, Suvarna Buradagunta2, Meshari Almeshari3
1Department of CST, Madanapalle Institute of Technology & Science, Madanapalle 517325, India.
Insights
This study introduces a hybrid deep learning model for classifying multiple types of intracranial hemorrhages (ICH) from CT scans. The Conv-LSTM approach achieves high accuracy, improving diagnosis for conditions like epidural and subdural bleeds.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Intracranial hemorrhage (ICH) is a critical condition requiring rapid diagnosis.
- Accurate classification of multiple ICH types (epidural, intraparenchymal, intraventricular, subarachnoid, subdural) is challenging.
- Current diagnostic methods rely on expert interpretation of CT scans.
Purpose of the Study:
- To develop and evaluate a multi-label classification model for detecting and classifying six types of ICH.
- To improve the accuracy and efficiency of ICH diagnosis using deep learning.
- To address the challenge of simultaneous multiple hemorrhages in patients.
Main Methods:
- A hybrid deep learning model combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) (Conv-LSTM) was developed.
- A Systematic Windowing technique was integrated with the Conv-LSTM model.
- Experiments were conducted using the publicly available RSNA dataset for ICH detection.
Main Results:
- The proposed Conv-LSTM model demonstrated high performance metrics: 93.87% sensitivity, 96.45% specificity, 95.21% precision, and 95.14% accuracy.
- The model effectively identifies the presence of hemorrhage and classifies its type(s).
- The F1 score results surpassed those of existing deep neural network-based algorithms.
Conclusions:
- The hybrid Conv-LSTM model offers a promising solution for accurate multi-label ICH classification from CT scans.
- This approach can aid clinicians in faster and more precise diagnosis of various intracranial hemorrhages.
- The model's performance suggests potential for integration into clinical workflows for improved patient outcomes.
Abstract:
Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient's skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.
