Design of inception ResNet V2 for detecting malarial infection using the cell image captured from microscopic slide

ABSTRACT

Over the past decades, malarial infection is considered a dreadful disease which ruins the lives of millions of people all over the globe. Several research works were developed based on machine learning algorithms to categorize the malarial infected person. However effective prediction with precise results is not attained in conventional approaches. For accurate prediction of malarial transmission deep learning technology is designed in this proposed model. This proposed design utilized inception ResNet V2 for the prediction of malarial-infected individuals. Before disease prediction, certain pre-processing techniques such as noise removal, contrast enhancement and segmentation are used to minimize the error rate during the classification process. The function of the proposed model is evaluated using metrics such as accuracy, recall, precision, etc. The simulation analysis shows that the proposed method obtains 0.98% accuracy, 0.02% error, precision is 0.92%, specificity is 0.94% so on. Thus the designed model predicted the malarial disease effectively.

KEYWORDS:

• Deep learning
• malarial transmission
• prediction model
• CNN
• cell image
• inception ResNetV2
• VGG19
• U-net

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No potential conflict of interest was reported by the author(s).

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Author contributions

The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-authors provide guidance to verify the analysis result and manuscript editing.

Compliance with ethical standards

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal’s editorial board decides not to accept it for publication.

Additional information

Notes on contributors

P. Mayil Vel Kumar

Dr. P. Mayil Vel Kumar B.E (ECE)., M.E(CSE)., PhD(CSE)., currently working as an associate professor, Dept of CSE, Karagam Institute of Technology, Coimbatore, India. He has 25 years of teaching experience. His area of specialization is data science. He published more than 25 articles in international journals and conferences. He is a reviewer for 5 international journals.

Anita Venaik

Dr. Anita Venaik is an academician with an experience of over 27 years in academics and industry. She worked with passion to develop a young mind with focus and zeal. She delivered quality content for holistic and humanistic development for the upcoming budding managers. All her work is aligned with the strategic goals of the university for the holistic development of the country and the youth of the Nation.

P. Shanmugaraja

Dr. P. Shanmugaraja obtained his doctor's degree in Information and Communication Engineering from Anna University. He has authored and co-authored over 20 publications in the areas of Computer Networks, Machine Learning, etc. He has guided more than 20 UG and PG scholars. His current research focusses on machine learning for flow classification and routing algorithms. He is a life member of ISTE. He has more than 20 years of experience in teaching. He is currently working as an associate professor, Department of Information Technology, Sona College of Technology, Salem, Tamilnadu.

P. John Augustine

Dr. P. John Augustine received his BE degree in Electronics and Communication Engineering from National Engineering College, Tamil Nadu, India in 1990 and his ME degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tamilnadu, India in 2004. He completed his doctoral programme at Anna University, Chennai under the faculty of Information and Communication Engineering (ICE) in 2018, Tamil Nadu, India. Having more than a decade of experience in both the IT industry and education, he is currently serving with Sri Eshwar College of Engineering as a professor in the Department of Computer Science and Engineering. His research expertise spans the fields of Mobile Computing, Optimization Techniques, Cloud Computing, Web Technology, Networking, Software Development Life Cycle models and IT Infrastructure Service models. Besides his academic involvement and various responsibilities, he is also an active member of ISTE.

M. Madiajagan

Prof. Dr. M. Madiajagan completed his B.E. in Computer Science and Engineering from Madras University, Chennai, Master's degree from BITS Pilani, Pilani, Rajasthan. He completed his Ph.D from BITS Pilani, Pilani, Rajasthan in 2009. Presently he is working as an associate professor at the School of Computer Science and Engineering, VIT, Vellore having rich teaching and research experience of more than 25 years and also served for 4 years as head of ICT in an International IT Industry and consultant of many International IT Projects. He published papers in many National and International journals, conferences and book chapters indexed by Elsevier, Scopus, Springer, DBLP, SCIE and many more. His areas of interest are Distributed Systems, Cyber Physical Systems, Brain Computer Interfaces, Network Security, Medical Imaging, Deep Learning and Neuro-Informatics.