Citation: Simge Çelebi, Mert Burkay Çöteli. Red and white blood cell classification using Artificial Neural Networks[J]. AIMS Bioengineering, 2018, 5(3): 179-191. doi: 10.3934/bioeng.2018.3.179
[1] | Nurtiti Sunusi, Giarno . Bias of automatic weather parameter measurement in monsoon area, a case study in Makassar Coast. AIMS Environmental Science, 2023, 10(1): 1-15. doi: 10.3934/environsci.2023001 |
[2] | RAHMOUN Ibrahim, BENMAMAR Saâdia, RABEHI Mohamed . Comparison between different Intensities of Rainfall to identify overflow points in a combined sewer system using Storm Water Management Model. AIMS Environmental Science, 2022, 9(5): 573-592. doi: 10.3934/environsci.2022034 |
[3] | Lei Wang, Huan Du, Jiajun Wu, Wei Gao, Linna Suo, Dan Wei, Liang Jin, Jianli Ding, Jianzhi Xie, Zhizhuang An . Characteristics of soil erosion in different land-use patterns under natural rainfall. AIMS Environmental Science, 2022, 9(3): 309-324. doi: 10.3934/environsci.2022021 |
[4] | Ronak P. Chaudhari, Shantanu R. Thorat, Darshan J. Mehta, Sahita I. Waikhom, Vipinkumar G. Yadav, Vijendra Kumar . Comparison of soft-computing techniques: Data-driven models for flood forecasting. AIMS Environmental Science, 2024, 11(5): 741-758. doi: 10.3934/environsci.2024037 |
[5] | Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim . Clustering analysis of PM2.5 concentrations in the South Sumatra Province, Indonesia, using the Merra-2 Satellite Application and Hierarchical Cluster Method. AIMS Environmental Science, 2022, 9(6): 754-770. doi: 10.3934/environsci.2022043 |
[6] | Swatantra R. Kethireddy, Grace A. Adegoye, Paul B. Tchounwou, Francis Tuluri, H. Anwar Ahmad, John H. Young, Lei Zhang . The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality. AIMS Environmental Science, 2018, 5(4): 273-293. doi: 10.3934/environsci.2018.4.273 |
[7] | Dong Chen, Marcus Thatcher, Xiaoming Wang, Guy Barnett, Anthony Kachenko, Robert Prince . Summer cooling potential of urban vegetation—a modeling study for Melbourne, Australia. AIMS Environmental Science, 2015, 2(3): 648-667. doi: 10.3934/environsci.2015.3.648 |
[8] | Zinabu A. Alemu, Emmanuel C. Dioha, Michael O. Dioha . Hydro-meteorological drought in Addis Ababa: A characterization study. AIMS Environmental Science, 2021, 8(2): 148-168. doi: 10.3934/environsci.2021011 |
[9] | Robert Russell Monteith Paterson . Depletion of Indonesian oil palm plantations implied from modeling oil palm mortality and Ganoderma boninense rot under future climate. AIMS Environmental Science, 2020, 7(5): 366-379. doi: 10.3934/environsci.2020024 |
[10] | Meriatna, Zulmiardi, Lukman Hakim, Faisal, Suryati, Mizwa Widiarman . Adsorbent performance of nipa (nypafruticans) frond in methylene blue dye degradation: Response surface methodology optimization. AIMS Environmental Science, 2024, 11(1): 38-56. doi: 10.3934/environsci.2024003 |
[1] | Shapiro MF, Sheldon G (1987) The Complete Blood Count and Leukocyte Differential Count: An Approach to Their Rational Application. J Emerg Med 106: 65–74. |
[2] | Lynch E C. (1990) Peripheral blood smear. Butterworths, Boston: Pubmed, 90: 1373–1377. |
[3] |
Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109. doi: 10.1093/biomet/57.1.97
![]() |
[4] | Ng HP, Ong SH, Foong KWC, et al. (2006) Medical image segmentation using k-means clustering and improved watershed algorithm. IEEE Southwest Symp Image Anal Interpret 106: 61–65. |
[5] |
Kurita T, Otsu N, Abdelmalek N (1992) Maximum likelihood thresholding based on population mixture models. Pattern Recogn 25: 1231–1240. doi: 10.1016/0031-3203(92)90024-D
![]() |
[6] |
Danielsson PE (1980) Euclidean distance mapping. Comput GraphImage Process 14: 227–248. doi: 10.1016/0146-664X(80)90054-4
![]() |
[7] | Sobel I (1990) An isotropic 3 × 3 image gradient operator. Mach vision three dimensional scenes, 376–379. |
[8] | Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. IEEE Int Conf Computl Intelli Meas Syst Appl CIMSA. 25: 96–101 |
[9] |
Sadeghian F, Seman Z, Ramli AR, et al. (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 11: 196. doi: 10.1007/s12575-009-9011-2
![]() |
[10] |
Jesus A, Georges F (2003) Automated detection of working area of peripheral blood smears using mathematical morphology. Anal Cell pathol 25: 37–49. doi: 10.1155/2003/642562
![]() |
[11] |
Goswami R, Pi D, Pal J, et al. (2015) Performance evaluation of a dynamic telepathology system (Panoptiq(™)) in the morphologic assessment of peripheral blood film abnormalities. Int J Lab hematol 37: 365–371. doi: 10.1111/ijlh.12294
![]() |
[12] | D'Ambrosio MV, Bakalar M, Bennuru S, et al. (2015) Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci Transl Med 7: 286re4. |
[13] | Manik S, Saini LM, Vadera N (2017) Counting and classification of White blood cell using Artificial Neural Network (ANN). IEEE Int Con Power Electron Intell Control Energy Syst 2017:1–5 |
[14] | Jia YQ, Shelhamer E, Donahue J, et al.(2014) Caffe: Convolutional architecture for fast feature embedding. Proc 22nd ACM Int Conf Multimedia, 675–678 |
[15] | Das DK, Maiti AK, Chakraborty C (2015) Automated system for characterization and classification of malaria infected stages using light microscopic images of thin blood smears. J Microsc-Oxford 257: 238–252. |
[16] | Automatic Peripheral Blood Smear and Slide Scanner Device. Available from: http://www.mantiscope.com |
[17] | Devi S, Singha J, Sharma M, et al. (2016) Erythrocyte segmentation for quantification in microscopic images of thin blood smears. J Intell Fuzzy Syst 4: 2847–2856. |
[18] |
Lee H, Chen YPP (2014) Cell morphology based classification for red cells in blood smear images. Pattern Recogn Lett 49: 155–161. doi: 10.1016/j.patrec.2014.06.010
![]() |
[19] | Amin MM, Kermani S, Talebi A, et al. (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using K-means clustering and support vector machine classifier. J Med Signal Sensor 5: 49. |
[20] | Li Y, Zhu R, Mi L, et al. (2016) Segmentation of white blood cell from acute Lymphoblastic Leukemia images using dual-threshold method. ComputMathMethod M 2016: 9514707. |
[21] | Linder N,Tukki R, Walliander M, et al. (2014) A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PLos One 9: e104855. |
[22] | Zhu C, Zheng Y, Luu K, et al. (2016) CMS-RCNN: contextual multi-scale region-based CNN for unconstrained face detection. In Bhanu B, Kumar A, Deep Learning for Biometrics, 3 Eds., Switzerland: Springer , 57–79. |
[23] | Beucher S, Mathmatique CDM (1991) The watershed transformation applied to image segmentation. Scanning Microsc Suppl 6: 299–314 |
[24] |
Dollar P, Wojek C, Schiele B, et al. (2012) Pedestrian detection: An evaluation of the state of the art. IEEE T Pattern Anal 34: 743. doi: 10.1109/TPAMI.2011.155
![]() |
[25] | Redmon J, Divvala S, Girshick R, et al. (2016) You only look once: Unified, real-time object detection. Comput Vision Pattern Recognit 2016: 779–788. |
[26] | He K, Zhang X, Ren S, et al. (2016) Deep residual learning for image recognition. IEEE conference on Compute Vison and Pattern Recogn, 770–778. |
[27] | Govind D, Lutnick B, Tomaszewski JE, et al. (2018). Automated erythrocyte detection and classification from whole slide images. J Med Imag 5: 027501. |
1. | Manuel Adrian Acuña-Zegarra, Daniel Olmos-Liceaga, Jorge X. Velasco-Hernández, The role of animal grazing in the spread of Chagas disease, 2018, 457, 00225193, 19, 10.1016/j.jtbi.2018.08.025 | |
2. | Lauren A. White, James D. Forester, Meggan E. Craft, Thierry Boulinier, Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges, 2018, 87, 00218790, 559, 10.1111/1365-2656.12761 | |
3. | Bruce Y. Lee, Sarah M. Bartsch, Laura Skrip, Daniel L. Hertenstein, Cameron M. Avelis, Martial Ndeffo-Mbah, Carla Tilchin, Eric O. Dumonteil, Alison Galvani, Ricardo E. Gürtler, Are the London Declaration’s 2020 goals sufficient to control Chagas disease?: Modeling scenarios for the Yucatan Peninsula, 2018, 12, 1935-2735, e0006337, 10.1371/journal.pntd.0006337 | |
4. | Vanessa Steindorf, Norberto Aníbal Maidana, Modeling the Spatial Spread of Chagas Disease, 2019, 81, 0092-8240, 1687, 10.1007/s11538-019-00581-5 | |
5. | Britnee A. Crawford, Christopher M. Kribs-Zaleta, Gaik Ambartsoumian, Invasion Speed in Cellular Automaton Models for T. cruzi Vector Migration, 2013, 75, 0092-8240, 1051, 10.1007/s11538-013-9840-7 | |
6. | Christopher M. Kribs, Christopher Mitchell, Host switching vs. host sharing in overlapping sylvaticTrypanosoma cruzitransmission cycles, 2015, 9, 1751-3758, 247, 10.1080/17513758.2015.1075611 | |
7. | N. El Saadi, A. Bah, T. Mahdjoub, C. Kribs, On the sylvatic transmission of T. cruzi, the parasite causing Chagas disease: a view from an agent-based model, 2020, 423, 03043800, 109001, 10.1016/j.ecolmodel.2020.109001 | |
8. | Cheol Yong Han, Habeeb Issa, Jan Rychtář, Dewey Taylor, Nancy Umana, Marc Choisy, A voluntary use of insecticide treated nets can stop the vector transmission of Chagas disease, 2020, 14, 1935-2735, e0008833, 10.1371/journal.pntd.0008833 | |
9. |
Daniel Olmos, Ignacio Barradas, David Baca-Carrasco,
On the Calculation of
R
0
Using Submodels,
2017,
25,
0971-3514,
481,
10.1007/s12591-015-0257-7
|
|
10. | Md. Abdul Hye, M. A. Haider Ali Biswas, Mohammed Forhad Uddin, Mohammad Saifuddin, Mathematical Modeling of Covid-19 and Dengue Co-Infection Dynamics in Bangladesh: Optimal Control and Data-Driven Analysis, 2022, 33, 1046-283X, 173, 10.1007/s10598-023-09564-7 | |
11. | A. Omame, H. Rwezaura, M. L. Diagne, S. C. Inyama, J. M. Tchuenche, COVID-19 and dengue co-infection in Brazil: optimal control and cost-effectiveness analysis, 2021, 136, 2190-5444, 10.1140/epjp/s13360-021-02030-6 | |
12. | Edem Fiatsonu, Rachel E. Busselman, Gabriel L. Hamer, Sarah A. Hamer, Martial L. Ndeffo-Mbah, Luisa Magalhães, Effectiveness of fluralaner treatment regimens for the control of canine Chagas disease: A mathematical modeling study, 2023, 17, 1935-2735, e0011084, 10.1371/journal.pntd.0011084 | |
13. | H. Rwezaura, S.Y. Tchoumi, J.M. Tchuenche, Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study, 2021, 27, 23529148, 100807, 10.1016/j.imu.2021.100807 | |
14. | Malicki Zorom, Babacar Leye, Mamadou Diop, Serigne M’backé Coly, Metapopulation Modeling of Socioeconomic Vulnerability of Sahelian Populations to Climate Variability: Case of Tougou, Village in Northern Burkina Faso, 2023, 11, 2227-7390, 4507, 10.3390/math11214507 | |
15. | Xuan Dai, Xiaotian Wu, Jiao Jiang, Libin Rong, Modeling the impact of non-human host predation on the transmission of Chagas disease, 2024, 00255564, 109230, 10.1016/j.mbs.2024.109230 | |
16. | M. Adrian Acuña-Zegarra, Mayra R. Tocto-Erazo, Claudio C. García-Mendoza, Daniel Olmos-Liceaga, Presence and infestation waves of hematophagous arthropod species, 2024, 376, 00255564, 109282, 10.1016/j.mbs.2024.109282 |