This paper is an attempt to distinguish the blood cells into classify between White Blood Corpuscles (WBC) and Red Blood Corpuscles RBC to further this classification to find the sickle cell detection. The sub categorization of the red blood corpuscles is an important implementation in this paper for the disease classification. The sickle cell anemia is a disease based on RBCs oxygen carrying capability. In order to avoid the misclassification the RBC sub-categorization is carried out. The sickle cell anaemic cells are found using the machine learning algorithms. The convolutional Neural Network based implementation is carried out to find the sickle and non-sickle cell RBCs. The results obtained are found to be satisfactory

Sickle Cell Anaemia, Convolutional Neural Network(CNN), Deep learning Methods

[1] Ruchika Garg , Asha Nigam , Prabhat Agrawal , Ashwini Nigam and Rachna Agrawal “Iron Carboxymaltose: A Safe and Effective Molecule to Combat Anemia in Pregnancy” , International Journal of Current Research and Academic Review ISSN: 2347-3215 Volume 4 Number 2 (February-2016) pp. 124-130
[2] http://www.who.int/mediacentre/factsheets/fs308/en/
[3] https://en.wikipedia.org/wiki/Dacrocyte
[4] https://en.wikipedia.org/wiki/Sickle-cell_disease
[5] http://www.cdc.gov/ncbddd/thalassemia/facts.html
[6] PranatiRakshita, KritiBhowmikb, “Detection of Abnormal Findings in Human RBC in Diagnosing Sickle Cell Anaemia Using Image Processing” , International Conference on Computational Intelligence: Modeling, Techniques and Applications (CIMTA- 2013).
[7] Shashi Bala, Amit Doegar, “Automatic Detection of Sickle cell in Red Blood cell using Watershed Segmentation”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2015.
[8] SiddharthSekharBarpanda,Prof. DiptiPatra,“Use of Image Processing Techniques to Automatically Diagnose Sickle-Cell Anemia Present in Red Blood Cells Smear” , Department of Electrical Engineering, National Institute of Technology Rourkela-769008 (ODISHA) May2013.
[9] Aguilar C, Vichinsky E, Neumayr L. “Bone and Joint Disease in Sickle Cell Disease”, HematolOncolClin North Am.; 19(5):929-4, Oct 2005.
[10] MenikaSahu, Amit Kumar Biswas, K. Uma, “Detection of Sickle Cell Anemia in Red Blood Cell: A Review”, International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-3, March 2015. S. Willium, “Network Security and Communication”, IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.
[11] Nathaniel Z. Piety ; Sergey S. Shevkoplyas,Paper-Based Diagnostics: Rethinking Conventional Sickle Cell Screening to Improve Access to High-Quality Health Care in Resource-Limited Settings,IEEE Pulse ,Volume: 8 , Issue: 3 , May-June 2017