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TensorFlow Based Prediction Model for Classifying Human Blood Smear Microscopic Images As Indicating Presence of Malaria Parasite


Affiliations
1 Department of Master of Computer Applications, RV College of Engineering, India., India
2 Department of Electronics and Communication Engineering, Sri Krishna Institute of Technology, India., India
     

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Malaria is now considered to be present in south Asian and African regions. Many countries are declared and certified as malaria free nations. But India is yet seen to be hosting with one or more indigenous cases. As per the World Health Organization (WHO) report India is one among the 25 nations expected to be malaria free by 2025. Currently the two variants out of four species of malaria parasite are prevalent in India. Falciparum and Vivax are the seen in several states of the country. The malaria detection is a manual procedure followed in the pathology laboratories. The human blood smears are collected and examined under the microscope. This process requires the experienced staff for better and accurate identification of the stages of the parasite detection. As per the standards specified for examination of a single slide is a minimum of 3 minutes and maximum of 5 minutes. But the process usually consumed by the pathology lab staff is minimum of 7 minutes with thick smears. Whereas the time duration required in case of thin smears is usually more. The proposed work aims at automating this process of detecting and identifying from thin blood smears without any compromise on the actual process adopted in detecting the parasite stages. The experiments are conducted on 200 images from Kaggle database, and the results obtained are encouraging.

Keywords

Malaria, Microscopic Image, Blood Smear, Tensor Flow, Prediction.
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  • Sivaramakrishnan Rajaraman, Stefan Jaeger and Sameer K. Antani, “Performance Evaluation of Deep Neural Ensembles Toward Malaria Parasite Detection in ThinBlood Smear Images”, PeerJ, Vol. 7, pp. 1-13, 2019.

  • Mehedi Masud, “Leveraging Deep Learning Techniques for Malaria Parasite Detection using Mobile Application”, Wireless Communications and Mobile Computing, Vol. 2020, pp. 1-14, 2020.

  • Yamala Sandhya, Prasanta Kumar Sahoo and K. Eswaran. ”Malaria Disease Detection using Deep Learning Technique”, International Journal on Advance Science and Technology, Vol. 29, No. 2, pp. 7736-7745, 2020.

  • Varun Magotra and Mukesh Kumar Rohil, “Malaria Diagnosis using a Lightweight Deep Convolutional Neural Network”, International Journal of Telemedicine and Applications, Vol. 2022, pp. 1-9, 2022.

  • Kishor Roy, “Detection of Malaria Parasite in Giemsa Blood Sample using Image Processing”, SSRN, Vol. 10, pp. 55-65, 2018.

  • A.A. Adenowo, Adesoji A. Awobajo and Sheriff Alimi, “Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood”, International Journal of Computer Applications, Vol. 975, pp. 8887-8898, 2020.

  • Golla Madhu, “Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images”, Machine Learning for Intelligent Decision Science, Vol. 87, pp, 191-209, 2020.

  • G.P. Gopakumar, M. Swetha and G.R.K. Sai Subrahmanyam, “Convolutional Neural Network-Based Malaria Diagnosis from Focus Stack of Blood Smear Images acquired using Custom-Built Slide Scanner”, Journal of Biophotonics, Vol. 11, pp. 1-16, 2018.


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  • TensorFlow Based Prediction Model for Classifying Human Blood Smear Microscopic Images As Indicating Presence of Malaria Parasite

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Authors

Preethi N Patil
Department of Master of Computer Applications, RV College of Engineering, India., India
Andhe Dharani
Department of Master of Computer Applications, RV College of Engineering, India., India
S. Anupama Kumar
Department of Master of Computer Applications, RV College of Engineering, India., India
Naganagouda Patil
Department of Electronics and Communication Engineering, Sri Krishna Institute of Technology, India., India

Abstract


Malaria is now considered to be present in south Asian and African regions. Many countries are declared and certified as malaria free nations. But India is yet seen to be hosting with one or more indigenous cases. As per the World Health Organization (WHO) report India is one among the 25 nations expected to be malaria free by 2025. Currently the two variants out of four species of malaria parasite are prevalent in India. Falciparum and Vivax are the seen in several states of the country. The malaria detection is a manual procedure followed in the pathology laboratories. The human blood smears are collected and examined under the microscope. This process requires the experienced staff for better and accurate identification of the stages of the parasite detection. As per the standards specified for examination of a single slide is a minimum of 3 minutes and maximum of 5 minutes. But the process usually consumed by the pathology lab staff is minimum of 7 minutes with thick smears. Whereas the time duration required in case of thin smears is usually more. The proposed work aims at automating this process of detecting and identifying from thin blood smears without any compromise on the actual process adopted in detecting the parasite stages. The experiments are conducted on 200 images from Kaggle database, and the results obtained are encouraging.

Keywords


Malaria, Microscopic Image, Blood Smear, Tensor Flow, Prediction.

References