An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks

Rajagopal, Manikandan; Buradagunta, Suvarna; Almeshari, Meshari; Alzamil, Yasser; Ramalingam, Rajakumar; Ravi, Vinayakumar

doi:10.3390/brainsci13030400

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…These investigations require extensive labelled image datasets as inputs to build the model [27]. Different approaches have been used for this purpose, from classifying slices as pathologic or normal to the automatic segmentation of abnormal areas [28][29][30][31]. Using AI models to accomplish iterative and tedious tasks such as blood segmentation is a significant advancement that reduces working times and allows large samples of patients to be to processed, increasing the statistical power of the clinical investigation.…”

Section: Discussionmentioning

confidence: 99%

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

García-García,

Cepeda,

Müller

et al. 2023

Brain Sciences

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Background: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. Methods: We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model’s architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Results: Images from 219 patients were processed; 175 for training and validation and 44 for the model’s evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). Conclusion: Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.

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Section: Discussionmentioning

confidence: 99%

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

García-García,

Cepeda,

Müller

et al. 2023

Brain Sciences

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“…However, the proposed work is limited to the overfitting problem, despite reaching 98% accuracy. A hybrid deep neural network was constructed in [ 17 ] to predict intracranial hemorrhage. CNNs and LSTMs are combined to implement systematic windowing.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Umapathy,

Murugappan,

Bharathi

et al. 2023

Diagnostics

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Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation–based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.

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“…An automated system for classifying types of cerebral hemorrhage based on image … (Areen Arabiat) 1595 CAD has gradually emerged as a prominent field of study in the area of diagnostic radiology and medical imaging [4], [5]. Sage and Badura [6] introduced a method for identifying various sorts of cerebral hemorrhages in head computed tomography images.…”

Section: Introductionmentioning

confidence: 99%

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

Arabiat,

Altayeb

2024

IJECE

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The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).

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An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks

Cited by 9 publications

References 35 publications

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

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