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BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan by Mushtaq, Muhammad Faheem (author);Shahroz, Mobeen (author);Aseere, Ali M. (author);Shah, Habib (author);Majeed, Rizwan (author);Shehzad, Danish (author);Samad, Ali (author) is a Engineering article available to read on EtoBox.

What is BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan about?

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

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Author

Mushtaq, Muhammad Faheem (author);Shahroz, Mobeen (author);Aseere, Ali M. (author);Shah, Habib (author);Majeed, Rizwan (author);Shehzad, Danish (author);Samad, Ali (author)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)