A Densely Connected Convolutional Network Towards Efficient Recognition of Intracranial Hemorrhage Using CT Images
DOI:
https://doi.org/10.3329/gubjse.v9i1.74880Keywords:
Intracranial Hemorrhage, CT images, DenseNet, Training, Testing, Recognition, RSNAAbstract
Intracranial Bleeding also known as Intracranial Hemorrhage (ICH) is a severe issue for human beings and the most common cause of it is trauma. Majorly ICH occurs due to hypertension and around 2.5 per 10,000 people get affected by one of its subtypes. The human brain consists of lots of soft tissue and nerves, that’s why it is so tough to find affected areas within the shortest period and to apply proper treatment or medication for a radiologist by analyzing the Computed Tomography (CT) images. In addition to ICH treatment being expensive, a densely connected convolutional network (DenseNet-169) model is recommended to accurately detect the damaged region quickly and affordably to facilitate the treatment of the patient. All were private and inaccessible, with the exception of the CQ500 and the Radiological Society of North America (RSNA) datasets about ICH. In our study, we employed stage-2 of the RSNA dataset, which comprises 121,232 test images and 752,803 training images. Phase-1 and phase-2 are the two stages of the dataset. Among the various preprocessing techniques, image type conversion, resizing, and normalization were performed on the dataset. During the learning phase of our model, for hyper-tuning, a portion (30%) of training data was utilized as validation data. The test data was then used to evaluate the model’s efficacy, and it was found that the ICH recognition accuracy of our developed model was 98%. Index Terms— Intracranial Hemorrhage, CT images, DenseNet, Training, Testing, Recognition, RSNA.
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