International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1553 MitoGame: Gamification Method for Detecting Mitosis from Histopathological Images Using Crowdsourcing Hanan Hussain 1 , Nirmala P.S 2 , Swathy M 3 1 M.Tech student, Computer Science, Vidya Academy of Science and Technology ,Kerala, India 2 M.Tech student, Computer Science, Vidya Academy of Science and Technology ,Kerala, India 3 M.Tech student, Computer Science, Vidya Academy of Science and Technology ,Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Learning from crowds using Gamification is a novel concept in medical Imaging. Convolution Neural Network can be designed to handle learning from crowds, using additional crowdsourcing layer. In biomedical context, ground truth labeling from non- expert crowd is generated using deep learning. Even though crowdsourcing is used for annotating a large number of online images, their feasibility for application in medical imaging context requires a deep knowledge. Hence Gamification task for detecting breast cancer requires correct instruction and Guidelines for the crowds. Noisy annotations can occur when an expert task like annotating mitosis detection are outsourced to non_expert users like crowds. Gamification in histopathological images is proposed so that complex tasks in biomedical domain in to a game for non experts . Further, analysis of the results from crowd and CNN shows that crowds do not underperform than medical experts. Key Words: Gamification, Crowdsourcing, Mitosis Detection, Histopathological Images, CNN, Deep Learning. 1.INTRODUCTION Crowdsourcing is a type of participative online activity in which an individual, an institution, a non- profit organization or a company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via flexible open call, the voluntary undertaking of a task [1]. The definition was made clear by Jeff Howe and Mark Robinson [2]. When new crowdsourcing frameworks were introduced recently IBM, Google and Microsoft are now focusing towards semi-automated computing for gathering ground-truth annotation data on images as well as videos in the medical domain. Recently, Celi et al. [3] conducted challenges, where pathologists, data scientists, and medical universities were invited to address specific tasks . As a part of this competition many new ideas and simulations are introduced. Deep Learning is a branch of Machine Learning based on a set of algorithms that attempt to model high level abstraction in data by using a deep graph with multiple processing layers , composed of multiple linear and non linear transformation. Widely used algorithm of deep learning in Image context are Convolutional Neural Networks , Since it explicitly implies that input is an image . CNN implementation provides higher accuracy in medical imaging [4]. The problem arises when the accuracy is obtained only when there are large number of training dataset. Since medical images are highly confidential and they are not available to public,obtaining data set has found to be difficult. Hence crowdsourcing platforms will helps the crowd to join with the pathologist and developers for converting problems to new prototype that can be implemented. 1.1 Gamification Gamification is the application of game design elements and game principles in non-game context. Crowdsourcing methodologies leveraging the contributions of citizen scientists connected via the Internet have recently proved to be of great value to solve certain scientific challenges involving big data analysis that cannot be entirely automated[5]. For example, Fold-It is an online game where players should solve puzzles that are in 3D by folding protein structures. This method should motivate the user to play using attractive graphics. Recent studies shows that 3 billion hours per week are spent by playing games around the globe. 1.2. Breast Cancer Grading This grading system takes into consideration three important factors which are [6].