An Efficient Hemorrhage Detection System using Decision Tree Classifier

Abstract

the visual representations of the inner constituents of body along with the functions of either organs or tissues comprising its physiology are developed in medical imaging. These images can be obtained by various techniques such as computed tomography (CT), magnetic resonant imaging (MRI), and x-ray. The objective of the system mentioned in this paper is to detect the presence of hemorrhage and to classify the type of it when detected. CT images are considered here to find the hemorrhage. Pre-processing techniques such as grayscale conversion, image resizing, edge detection and sharpening are done to make the input image suitable for further processing. After preprocessing the images go through morphological operations to help identify the shape related features in correspondence to the hemorrhage. Sobel and markers are used in the processed ct image to highlight the interested region. Then watershed algorithm is employed for the purpose of segmentation. The presence of hemorrhage can be detected as a result of segmentation. Once hemorrhage is detected feature extraction is done to classify its type. Active contours are drawn and features extracted are fed to the decision tree. The classifier helps in finding the type of hemorrhage with the detected features. The classifier result can be viewed, interpreted and evaluated by medical assistance. The aim of this research is to increase the chance of predicting hemorrhage in the image and then to classify its type. The proposed system classifies three types of hemorrhages. The average accuracy of the system in classifying the three types of hemorrhage is found as 98%

Intracranial Hemorrhage Detection and Classification of its Subtypes using CNN, ResNet, Random Forest and DenseNet201

“…The system's classi cation accuracy for the three forms of haemorrhage was found to be 98% on average. [15] Ammar Mohammed & Lamri Mohamed & Saïd Mahmoudi & Laidi Amel., designed a tool to help radiologists to identify Intracranial Haemorrhage and each of their subtypes by diagnosing the CT scan images. Five kind of deep learning models: ResNet 50, VGG16, Xception, InceptionV3, InceptionResnetV2 had been used here.…”