BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

Abstract

Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

“…Several previous studies [ 32 , 33 , 38 – 40 ] also employed binary-class classification problems for predicting other targets related to TBI lesions. The algorithm proposed in by Liu et al [ 40 ] aimed to distinguish normal CT slices from abnormal ones, including 5 types of hemorrhage (epidural/extradural hemorrhage [EDH], subdural hemorrhage [SDH], subarachnoid hemorrhage [SAH], intraparenchymal hemorrhage [IPH], and intraventricular hemorrhage [IVH]).…”

“…Several previous studies [32,33,[38][39][40] also employed binary-class classification problems for predicting other targets related to TBI lesions. The algorithm proposed in by Liu et al [40] aimed to distinguish normal CT slices from abnormal ones, including 5…”

“…•Any hematoma [32,33] •ICH [34][35][36][37] •Normal/abnormal [38][39][40] •ICP level (high/low) [41][42][43] •Hematoma expansion [44] •Any hematoma [45][46][47][48] •ICH [49][50][51][52][53][54][55] •SDH [56,57] •Normal/abnormal [58] •ICH [59] •SDH [60] •Normal/Abnormal [61] Others •Midline delineation [62][63][64] •Cisterns [59] •Midline [59]…”

Optimizing CNN‐LSTM hybrid classifier using HCA for biomedical image classification

“…Mainly MRI (Pradhan, Das, et al, 2021) and computed tomography (CT) scan (Ugwuanyi et al, 2020; Wijdicks, 2018) were utilized to determine whether the patient had a brain haemorrhage or not. The brain haemorrhage was classified using Naive Bayes (Xu et al, 2020), K‐Means clustering (Gao et al, 2020), Image Segmentation (Haque & Neubert, 2020), Multi‐class classification (Hsu et al, 2019), Recurrent Neural Network (RNN) (Sherstinsky, 2020), LSTM (Burduja et al, 2020), CNN (Mushtaq et al, 2021), and hybrid models (Patel et al, 2019).…”

“…Brain Haemorrhage is the term for internal bleeding in the brain (Fallenius et al, 2019). These disorders are caused by a rapid blood clot (Otterskog et al, 2016) in the veins that will provide blood to the internal bleeding in the brain's tissue fluid due to artery rupturing (Abraham & Chang, 2016; Chithaluru, Al‐Turjman, et al, 2021; Chithaluru, Fadi, et al, 2021; Mushtaq et al, 2021). These are the leading causes of death.…”