Online ISSN: 2515-8260

Keywords : intracranial haemorrhage

Role of computed tomography (CT) in cerebrovascular accidents: A tertiary care hospital based study

Dr. Asif Majid Wani, Dr. Peerzada Ziaulhaq, Dr. Najeeb Tallal Ahangar, Dr. Majid Jehangir

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 2800-2806

Background: Stroke specifically the type due to cerebrovascular disease is defined as a sudden, non-convulsive focal neurological deficit. The terms “apoplexy” originating from the Greek and insult from the Latin “insultus” described stroke phenomenon in ancient times. Cerebrovascular accident is a leading cause of death and disability throughout the world. It is a common cause of death after heart disease and cancer in India. Accurate and early diagnosis can improve the morbidity and mortality rates, as newer and more effective therapies are currently being instituted. Since computed tomography imaging is widely available, cost effective and less time consuming, it plays the role of first line imaging modality.
Aims and objectives: The purpose of the present study is to document the presence or absence of haemorrhage or infarcts, to determine the location and reasonably assessing the territory to blood vessels involved and to detect the incidence of negative cases of clinically suspected stroke.
Materials  and  Methods:  A prospective study of 62 cases admitted to Department of Radiodiagnosis, Government Medical College, Baramulla, Jammu and Kashmir, India with the clinical diagnosis of acute stroke were taken up for the study. Data for my study is collected by sampling referred cases with a clinical history of stroke.
Results: Out of 62 patients clinically suspected of CVA submitted for CT scan study of the brain. 40 patients i.e., 64.5% had infarcts. 15 patients i.e., 24.2% had haemorrhage, 3 patient i.e., 5% had S.D.H., 2 patients i.e., 3.33% had C.V.T. 1 patient i.e., 1.6 % had tumour and 1 patient i.e., 1.6% had normal scans. Infarcts formed the major group of the CVA cases i.e., 64.5%, involving most commonly the R.M.C.A. territory in patients i.e., 26.31%. Haemorrhage formed the second major group of the CVA cases i.e., 25%, involving most commonly the L.M.C.A. territory in patients i.e., 26.66%. 

Brain Tumor And Intracranial Haemorrhage Feature Extraction And Classification Using Conventional And Deep Learning Methods

R. Aruna Kirithika; S. Sathiya; M. Balasubramanian; P. Sivaraj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 237-258

Presently, brain tumor (BT) and Intracranial hemorrhage (ICH) detection and classification processes become essential to save human lives. Automated diagnosis model using deep learning (DL) models finds useful to attain improved diagnostic outcome. This paper presents an ensemble of handcrafted and deep features for BT and ICH diagnosis. The proposed model comprises of three important processes, such as preprocessing, feature extraction and classification. The preprocessing of the input image takes place in three ways namely skull stripping, bilateral filtering (BF) and contrast limited adaptive histogram equalization (CLAHE) based contrast enhancement. In addition, scale invariant feature transform (SIFT) and AlexNet models are used for feature extraction process. In order to classify the existence of BT and ICH, two classification models is carried out such as gaussian naïve bayes (GNB) and random forest (RF).For validating the effective diagnostic performance of the proposed model, a set of simulations were carried out to determine the different class labels. The simulation outcome indicated the effective performance with the maximum sensitivity of 92.41%, specificity of 100%, and accuracy of 94.26%.