An Automated Classification Of Intracranial Haemorrhage Using Deep Convolution Neural Network Model
Traumatic brain injury might result to intracranial haemorrhage (ICH). The ICH could become a major disability or mortality when it is not precisely and timely diagnosed at the earlier stage. Due to the advanced developments in the deep learning models, automated medical diagnosis models can be developed to solve complicated decision making problem in healthcare sector. Keeping this in mind, this paper presents a new automated DL based segmentation and classification model for ICH diagnosis.The proposed method initially undergoes a set of preprocessing technique to improve the quality of the input images. Besides, instance segmentation model using DL based Depthwise Separable Network is employed to perform the segmentation process, called ISM-DL, thereby the injured regions in the brain can be identified. The proposed model also uses scale-invariant feature transform (SIFT) and residual network (ResNet) are used for feature extraction process. At last, a set of three machine learning (ML) classifiers namely logistic regression (LR), multilayer perceptron (MLP), and gradient boosting tree (GBT) are employed to determine the appropriate class labels of ICH. The performance of the proposed models are evaluated on the benchmark ICH dataset and the experimental outcome stated that the proposed model outperformed the compared methods under different aspects. Accuracy rate of 96.95% was achieved from the proposed methods using convolutional Neural Network-residual network (CNNRN).