Affiliations: [a] Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India | [b] Ataturk Education Faculty, Near East University, North Nicosia, Cyprus
Correspondence:
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Corresponding author: Nikita Jain, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India. E-mail: Nikita.jain@bharatividyapeeth.edu.
Abstract: Malaria is a protozoan disease that is affecting the 200 million lives of the people around the world and around 4 lakhs death per year due to this which raises our concern and we have tried to target the most affected part in the world i.e. Africa. In the paper approach is to maximize the recent developments in the area of malaria detection using cell images using Convolutional Neural Network (CNN). We have tried to automate the processes which are indulged in the detection of malaria. The method with no pre-processing and no high ended GPU dependency produces an accuracy of 97% proving it to be an efficient as well as low cost detection algorithm. The given implementation can easily detect malaria even from blurred images with no initial pre-processing needed. The algorithm is further compared with standard classification algorithms and stands out be highly efficient in terms of precision, recall, F1 score and computation time.
Keywords: Malaria, CNN, F1-score, validation, training, loss
