New Normal and Abnormal Red Blood Cells Features for Improved Classification
This paper focused obtaining new features for improved classification of red blood cells (RBCs). RBCs varies according to shapes, colors and sizes. Abnormal RBCs may be caused by anemia. Abnormal RBCs has great similarities among each other causing difficulties in medical diagnosis. In this work, spatial, spectral statistical features and geometrical features of RBCs are extracted from 1000 normal and abnormal RBCs. The extracted features are reduced using Principal Component Analysis (PCA) and tested with different types of machine learning algorithms for classification. Classifications were evaluated for high sensitivity, specificity, and kappa statistical parameters. The classifications yielded accuracy rates of 97.9%, 98% and 98% for discriminative (SVM), generative (RBFNN) and clustering (K-NN) algorithm respectively, which is an improvement over previous works.
Savkare, S. S., and S. P. Narote. "Blood cell segmentation from microscopic blood images." Information Processing (ICIP), 2015 International Conference on. IEEE, 2015.
Apostolopoulos, G. T. (2010). Recognition and identification of red blood cell size using angular radial transform and neural networks. In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 .
J. Ford (2013). Red blood cell morphology, International Journal of Laboratory Hematology, Volume 35, Issue 3,June 2013 ,Pages 351–357.
Jones, K. W. (2009). "Evaluation of cell morphology and introduction to platelet and white blood cell morphology". Clinical Hematology and Fundamentals of Hemostasis , 93-116.
Jameela Ail Alkrimi, L. E.-J. (2014)."Isolation and Classification of Red Blood Cells in Anemic Microscopic Images". World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering 8, no. 10, 8(10), 727-730.
Danglade, F., Veron, P., Pernot, J. P., & Fine, L. (2015). Estimation of CAD model simplification impact on CFD analysis using machine learning techniques.
Domingos, P. (2012). A few useful things to know about machine learning. . Communications of the ACM,, 55(10), 78-87.
Elsawy, A. S. (2013). Principal component analysis ensemble classifier for P300 speller applications. 8th International Symposium on In Image and Signal Processing and Analysis (ISPA), 2013, 444-44.
Martín-Fernández, J. A., Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosona-Delgado, R. (2017). Advances in Principal Balances for Compositional Data. Mathematical Geosciences, 1-26.
David, Charles C., and Donald J. Jacobs (2014). Principal component analysis: a method for determining the essential dynamics of proteins. In Protein dynamics (pp. 193-226). Humana Press, Totowa, NJ.
Matricardi, M. (2010). A principal component based version of the RTTOV fast radiative transfer model. Quarterly Journal of the Royal Meteorological Society, 136(652), 1823-1835.
Nandi, D. A. (2015). Principal component analysis in medical image processing:a study. . International Journal of Image Mining, 1(1), 65-86.
Vincent, I., Shin, B. K., Kwon, S. G., Lee, S. H., & Kwon, K. R. (2014, July). Feature Selection using Principal Component Analysis for Leukemia Classification. In Proceeding of the 10th International Conference on Multimedia Information Technology and Applications 2014 (pp. 206-207).
Park, H. S. (2016). Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. PloS one, 11(9).
Sharma, N. M. (2012). Color image segmentaion techniques and issues: an approach. . International Journal of Scientific & Technology Research, 1(4), 9-12.
Wheeless, L. L. (1994). Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature. Cytometry, 17(2), 159-166.
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research common errors and some comment on improved practice.Educational and Psychological measurement, 66(3), 393-416.
Gibson, Ian, and Christopher Amies. "Data normalization techniques." U.S. Patent No. 6,259,456. 10 Jul. 2001.
Abdi, H. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
Authors who submit papers with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- By submitting the processing fee, it is understood that the author has agreed to our terms and conditions which may change from time to time without any notice.
- It should be clear for authors that the Editor In Chief is responsible for the final decision about the submitted papers; have the right to accept\reject any paper. The Editor In Chief will choose any option from the following to review the submitted papers:A. send the paper to two reviewers, if the results were negative by one reviewer and positive by the other one; then the editor may send the paper for third reviewer or he take immediately the final decision by accepting\rejecting the paper. The Editor In Chief will ask the selected reviewers to present the results within 7 working days, if they were unable to complete the review within the agreed period then the editor have the right to resend the papers for new reviewers using the same procedure. If the Editor In Chief was not able to find suitable reviewers for certain papers then he have the right to reject the paper.
- Author will take the responsibility what so ever if any copyright infringement or any other violation of any law is done by publishing the research work by the author
- Before publishing, author must check whether this journal is accepted by his employer, or any authority he intends to submit his research work. we will not be responsible in this matter.
- If at any time, due to any legal reason, if the journal stops accepting manuscripts or could not publish already accepted manuscripts, we will have the right to cancel all or any one of the manuscripts without any compensation or returning back any kind of processing cost.
- The cost covered in the publication fees is only for online publication of a single manuscript.