One of the most serious forms of brain stroke is intracranial hemorrhage (ICH). When an artery bursts, the brain and the tissue around the artery start bleeding. This study proposes a joint feature selection strategy to classify computed tomography (CT) images of intracranial hemorrhage. The joint feature set is composed of transform and texture features. Joint features are constructed from a combination of grey level co-occurrence matrix (GLCM) features, discrete wavelet features (DWT), and discrete cosine features (DCT). Brain hemorrhage CT image classification uses ensemble-based machine learning (ML) techniques. On the training dataset, a Synthetic Minority Over-Sampling Technique (SMOTE) is applied to treat the problem of oversampling by adding fresh data. Additionally, the sequential forward feature selection technique is used to obtain feature subsets. The classification accuracy is further examined for varied feature vector sizes. Confusion matrix, precision, and recall in categorization are employed as performance evaluation measurements. The ML-based ensemble classifiers can produce highly accurate results with the aid of the proposed novel feature extraction mechanism. When taking into consideration a crucial feature set consisting of six features, it can be seen that Random Forest obtained the greatest accuracy, which is 87.22%. 

A novel machine learning-based feature extraction method for classifying intracranial hemorrhage computed tomography images

Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques for intracranial hematoma detection in non-contrast computed tomography images cannot effectively detect multiple lesions of irregular sizes and shapes. Therefore, a computer-aided system using different window settings, YOLOv5s, cascaded attention module, and spatial pyramid pooling-fast is proposed to detect hematoma types, namely acute intraparenchymal, intraventricular, subdural, epidural, subarachnoid, and chronic subdural. Firstly, the computed tomography images are pre-processed using a window-based stacking approach wherein a three-channel image is generated by stacking grayscale images obtained with the aid of multiple window settings, i.e, brain, bone, and subdural. Secondly, a cascaded attention module is constructed in the neck of the YOLOv5s model to improve its detection performance by placing the convolution block attention module in serial with the efficient channel attention module. The cascaded attention module enriches the feature representation of various hematoma types in complex backgrounds especially when they are small and inconspicuous. The spatial pyramid pooling is replaced by a spatial pyramid pooling-fast to reduce the computational parameters and accelerate the feature fusion ability. The proposed deep learning model is trained, validated, and tested with 15,921 images from the brain haemorrhage extended dataset and it achieved overall precision, recall, F1-score, and mean average precision at 0.5, and mean average precision at 0.5:0.95 of 0.935, 0.908, 0.921, 0.943 and 0.65 respectively. The experimental results show that in comparison to the original YOLOv5s model and state-of-the-art methods, the model was able to localize and classify the acute or chronic instances of five hematoma subtypes in an individual image with improved precision and recall values. Hence the proposed system can be used in hospitals for the early and accurate detection of hematoma.

YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for types of Intracranial Hematoma in CT Images

Intracerebral hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-contrast head CT imaging. ICH is the most prevalent kind of hemorrhagic stroke that occur due to rupture of cerebral blood vessel. Internal bleeding is one of the major reason for ICH. It is important to diagnose ICH as soon as possible; however, manual diagnostic techniques require assistance from an imaging specialist and it takes a lot of time. And also doctor will need years of training to quickly diagnose and locate it before beginning treatment. Deep learning technology is used to automate the process and assist radiologists for more prompt and better decision-making. This paper an exhaustive study is carried out brain intracerebral hemorrhage classification. Comparative study is done to identify the best feature selection that suits for huge dimensions of the data. Deep learning (DL) algorithms such as convolutional neural network (CNN) and transfer learning are used for classification that also give accurate result. The research gap identified results reveals that the effectiveness of the better proposed model is required for accurate prediction and saves the life of the patient. 

A Comparative Study on Brain Intracerebral Hemorrhage Classification Using Head CT Scan for Stroke Analysis

Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients. 

An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

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Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

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Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework