An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection Sitara Afzal 1 , Muazzam Maqsood 1,* , Irfan Mehmood 2 , Muhammad Tabish Niaz 3 and Sanghyun Seo 4 1 Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan 2 Department of Media Design and Technology, Faculty of Engineering & Informatics, University of Bradford, Bradford, BD7 1AZ, UK 3 Department of Smart Device Engineering, School of Intelligent Mechatronics Engineering, Sejong University, Seoul, South Korea 4 School of Computer Art, College of Art & Technology, Chung-Ang University, Anseong, 17546, South Korea  Corresponding Author: Muazzam Maqsood. Email: muazzam.maqsood@cuiatk.edu.pk Received: 27 August 2020; Accepted: 26 September 2020 Abstract: Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer ’ s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for indivi- duals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deteriora- tion and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’ s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discrimina- tive feature extraction specific to CMBs (iv) classification using a five layers con- volutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods. Keywords: Microbleeds detection; false-positive; deep learning; CNN 1 Introduction Cerebral Microbleeds (CMBs) are chronic body fluid products having minor weights that are often found in patients. Such patients are tormented by different diseases including TBI, Alzheimer ’ s Disease (AD), and stroke [1]. The position of these CMBs shows etiology. The amount of these CMBs can specify the severity of the possible cognitive impairment and intracerebral hemorrhage (ICH) [2]. Deep CMBs in the thalamus are characteristically connected with hypertension, while the occurrence of labor CMBs may also recommend cerebral amyloid angiopathy. These are the conditions in which protein components in the brain amyloid starts building up in the cerebral [3]. Identifying CMBs could be This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computers, Materials & Continua DOI:10.32604/cmc.2021.013966 Article ech T Press Science