212 related articles for article (PubMed ID: 32222951)
1. Diagnosis support of sickle cell anemia by classifying red blood cell shape in peripheral blood images.
Delgado-Font W; Escobedo-Nicot M; González-Hidalgo M; Herold-Garcia S; Jaume-I-Capó A; Mir A
Med Biol Eng Comput; 2020 Jun; 58(6):1265-1284. PubMed ID: 32222951
[TBL] [Abstract][Full Text] [Related]
2. Red blood cell cluster separation from digital images for use in sickle cell disease.
González-Hidalgo M; Guerrero-Peña FA; Herold-García S; Jaume-I-Capó A; Marrero-Fernández PD
IEEE J Biomed Health Inform; 2015 Jul; 19(4):1514-25. PubMed ID: 25216490
[TBL] [Abstract][Full Text] [Related]
3. Erythrocyte shape classification using integral-geometry-based methods.
Gual-Arnau X; Herold-García S; Simó A
Med Biol Eng Comput; 2015 Jul; 53(7):623-33. PubMed ID: 25773368
[TBL] [Abstract][Full Text] [Related]
4. A deep convolutional neural network for classification of red blood cells in sickle cell anemia.
Xu M; Papageorgiou DP; Abidi SZ; Dao M; Zhao H; Karniadakis GE
PLoS Comput Biol; 2017 Oct; 13(10):e1005746. PubMed ID: 29049291
[TBL] [Abstract][Full Text] [Related]
5. A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.
S A; Ganesan K; K BB
Biomed Tech (Berl); 2023 Apr; 68(2):175-185. PubMed ID: 36197949
[TBL] [Abstract][Full Text] [Related]
6. Morphologic studies of sickle erythrocytes by image analysis.
Horiuchi K; Ohata J; Hirano Y; Asakura T
J Lab Clin Med; 1990 May; 115(5):613-20. PubMed ID: 2341764
[TBL] [Abstract][Full Text] [Related]
7. Diagnostic value of anemia, red blood cell morphology, and reticulocyte count for sickle cell disease.
Losek JD; Hellmich TR; Hoffman GM
Ann Emerg Med; 1992 Aug; 21(8):915-8. PubMed ID: 1497156
[TBL] [Abstract][Full Text] [Related]
8. A Review of Automated Methods for the Detection of Sickle Cell Disease.
Das PK; Meher S; Panda R; Abraham A
IEEE Rev Biomed Eng; 2020; 13():309-324. PubMed ID: 31107662
[TBL] [Abstract][Full Text] [Related]
9. Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature.
Wheeless LL; Robinson RD; Lapets OP; Cox C; Rubio A; Weintraub M; Benjamin LJ
Cytometry; 1994 Oct; 17(2):159-66. PubMed ID: 7835166
[TBL] [Abstract][Full Text] [Related]
10. Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia.
Das DK; Chakraborty C; Mitra B; Maiti AK; Ray AK
J Microsc; 2013 Feb; 249(2):136-49. PubMed ID: 23252834
[TBL] [Abstract][Full Text] [Related]
11. Identification and red blood cell automated counting from blood smear images using computer-aided system.
Acharya V; Kumar P
Med Biol Eng Comput; 2018 Mar; 56(3):483-489. PubMed ID: 28815426
[TBL] [Abstract][Full Text] [Related]
12. New Methods for Morphological Erythrocytes Classification.
Herold-Garcia S; Fernandes LAF
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():4068-4071. PubMed ID: 31946765
[TBL] [Abstract][Full Text] [Related]
13. Irreversibly sickled erythrocytes in sickle cell anemia: a quantitative reappraisal.
Rodgers GP; Noguchi CT; Schechter AN
Am J Hematol; 1985 Sep; 20(1):17-23. PubMed ID: 4025318
[TBL] [Abstract][Full Text] [Related]
14. Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs.
Rehman A; Abbas N; Saba T; Mahmood T; Kolivand H
Microsc Res Tech; 2018 Jul; 81(7):737-744. PubMed ID: 29637666
[TBL] [Abstract][Full Text] [Related]
15. An automated method of differential red blood cell classification with application to the diagnosis of anemia.
Bacus JW; Weens JH
J Histochem Cytochem; 1977 Jul; 25(7):614-32. PubMed ID: 330716
[TBL] [Abstract][Full Text] [Related]
16. Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review.
K T N; Prasad K; Singh BMK
Med Biol Eng Comput; 2022 Sep; 60(9):2445-2462. PubMed ID: 35838854
[TBL] [Abstract][Full Text] [Related]
17. Morphology of sickle cells produced in solutions of varying osmolarities.
Hijiya N; Horiuchi K; Asakura T
J Lab Clin Med; 1991 Jan; 117(1):60-6. PubMed ID: 1987310
[TBL] [Abstract][Full Text] [Related]
18. Healthy and unhealthy red blood cell detection in human blood smears using neural networks.
Elsalamony HA
Micron; 2016 Apr; 83():32-41. PubMed ID: 26867209
[TBL] [Abstract][Full Text] [Related]
19. Size and density measurements of single sickle red blood cells using microfluidic magnetic levitation.
Goreke U; Bode A; Yaman S; Gurkan UA; Durmus NG
Lab Chip; 2022 Feb; 22(4):683-696. PubMed ID: 35094036
[TBL] [Abstract][Full Text] [Related]
20. Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.
Praljak N; Iram S; Goreke U; Singh G; Hill A; Gurkan UA; Hinczewski M
PLoS Comput Biol; 2021 Nov; 17(11):e1008946. PubMed ID: 34843453
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
