Intracranial Hemorrhage Detection in Ct Scan Using Deep Learning

Missed detection of intracranial hemorrhage in Head CT scans has significantly impacted patient morbidity and mortality. Early detection of intracranial hemorrhage enables patients to receive appropriate treatment which resulted in a better outcome. Some doctors have limited experience in interpreting the CT scan hence increasing the probability to miss the hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. All of the samples have been anonymized into secondary data. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, a confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with an F1 score of 0.9500. This study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. This dataset is owned by Abdul Kader Helwan, an academic staff at Al-Manar University of Tripoli, Lebanon. Permission to use the dataset for this research was officially obtained from the owner. All of samples have been anonymized into secondary data. The data is divided into train, validation, and test samples. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with F1 score of 0.9500. Hence, this study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage.