Anemia Detection using a Deep Learning Algorithm by Palm Images

Main Article Content

S. Dhanasekaran
N. R. Shanker


Our aim is to detect anemia through a comparative analysis of three convolutional neural network (CNN) models, namely EfficientNet B3, DenseNet121, and CNN AllNet. A collection of 3,000 microscopic palm pictures, including 1,500 anaemic and 1,500 non-anemic samples, was used to train and test the algorithms. The dataset was preprocessed to balance the classes, augment the images, and normalize the pixel values. The models were trained using transfer learning on the ImageNet dataset and fine-tuned on the anemia dataset. The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. The results showed that CNN ALLNET achieved the highest accuracy of 96.8%, followed by DenseNet121 with 94.4%, and EfficientNet B3 with 91.2%. The precision, recall, and F1-score also followed a similar trend. The study concludes that CNN ALLNET is the optimal model for anemia detection due to its high accuracy and overall better performance when compared with the different models. The findings of this research could provide a basis for further studies on anemia detection using CNN models, ultimately improving the accuracy and efficiency of anemia diagnosis and treatment.

Article Details

How to Cite
Dhanasekaran, S., & Shanker, N. R. . (2023). Anemia Detection using a Deep Learning Algorithm by Palm Images. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 79–89.


M. Rivero-Palacio, W. Alfonso-Morales, and E. Caicedo-Bravo, “Mobile Application for Anemia Detection through Ocular Conjunctiva Images,” 2021 IEEE Colomb. Conf. Appl. Comput. Intell. ColCACI 2021 - Proc., pp. 8–13, 2021, doi: 10.1109/ColCACI52978.2021.9469593.

F. Akalin and N. Yumu?ak, “Detection and classification of white blood cells with an improved deep learning-based approach,” Turkish J. Electr. Eng. Comput. Sci., vol. 30, no. 7, pp. 2725–2739, 2022, doi: 10.55730/1300-0632.3965.

A. A. Shvets, V. I. Iglovikov, A. Rakhlin, and A. A. Kalinin, “Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks,” 2018 17th IEEE Int. Conf. Mach. Learn. Appl., no. 1, pp. 1–6, 2018.

V. Ranga, S. Gupta, P. Agrawal, and J. Meena, “Pathological Analysis of Blood Cells Using Deep Learning Techniques,” Recent Adv. Comput. Sci. Commun., vol. 15, no. 3, pp. 1–6, 2020, doi: 10.2174/2666255813999200904113251.

N. Praveen, N. S. Punn, S. K. Sonbhadra, S. Agarwal, M. Syafrullah, and K. Adiyarta, “White blood cell subtype detection and classification,” White blood cell subtype Detect. Classif., vol. 2021-Octob, pp. 203–207, 2021, doi: 10.23919/EECSI53397.2021.9624268.

T. Xia, Y. Q. Fu, N. Jin, P. Chazot, P. Angelov, and R. Jiang, “AI-enabled Microscopic Blood Analysis for Microfluidic COVID-19 Hematology,” Proc. - 2020 5th Int. Conf. Comput. Intell. Appl. ICCIA 2020, pp. 98–102, 2020, doi: 10.1109/ICCIA49625.2020.00026.

A. B. Chowdhury, J. Roberson, A. Hukkoo, S. Bodapati, and D. J. Cappelleri, “Automated complete blood cell count and malaria pathogen detection using convolution neural network,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 1047–1054, 2020, doi: 10.1109/LRA.2020.2967290.

T. Mazzu-Nascimento et al., “Smartphone-based photo analysis for the evaluation of anemia, jaundice and COVID-19,” Int. J. Nutrology, vol. 14, no. 02, pp. e55–e60, 2021, doi: 10.1055/s-0041-1734014.

M. L.?; B. N.?; S. S.?; N. K. Sichani, “The Detection Of Dacrocyte , Schistocyte and Elliptocyte cells in Iron Deficiency Anemia,” 2015 2nd Int. Conf. Pattern Recognit. Image Anal., pp. 1–5, 2015, doi: 10.1109/PRIA.2015.7161628.

P. T. Dalvi and N. Vernekar, “Anemia Detection using Ensemble Learning Techniques and Statistical Models,” IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol., pp. 1747–1751, 2016.

C. C. Hortinela, J. R. Balbin, J. C. Fausto, P. Daniel Divina, and J. P. T. Felices, “Identification of Abnormal Red Blood Cells and Diagnosing Specific Types of Anemia Using Image Processing and Support Vector Machine,” 2019 IEEE 11th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. HNICEM 2019, 2019, doi: 10.1109/HNICEM48295.2019.9072904.

B. Cells, “Computer Aided Detection of Abnormal Red Blood Cells,” IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol., no. May 20-21, pp. 1741–1746, 2016.

J. A. D. C. A. Jayakody, E. A. G. A. Edirisinghef, and S. Lokuliyana, “HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection,” Cogn. Eng. Next Gener. Comput. A Pract. Anal. Approach, pp. 93–119, 2021, doi: 10.1002/9781119711308.ch3.

Sivakumar, D. S. (2021). Clustering and Optimization Based on Hybrid Artificial Bee Colony and Differential Evolution Algorithm in Big Data. Research Journal of Computer Systems and Engineering, 2(1), 23:27. Retrieved from

M. J. M.?; V. V. Q.?; A. B.?; J. D. C.?; M. V. Caya, “White Blood Cell Classification and Counting Using Convolutional Neural Network,” 2018 3rd Int. Conf. Control Robot. Eng., pp. 259–263, 2018, doi: 10.1109/ICCRE.2018.8376476.

S. Raina, A. Khandelwal, S. Gupta, and A. Leekha, “Blood cells detection using faster-RCNN,” 2020 IEEE Int. Conf. Comput. Power Commun. Technol. GUCON 2020, pp. 217–222, 2020, doi: 10.1109/GUCON48875.2020.9231134.

M. Tyagi, L. M. Saini, and N. Dahyia, “Detection of Poilkilocyte Cells in Iron Deficiency Anaemia Using Artificial Neural Network,” Int. Conf. Comput. Power, Energy Inf. Commun. Detect., pp. 108–112, 2016, doi: 10.1109/ICCPEIC.2016.7557233.

T. Richa, U.N. Aishwarya, R. Akarsh, and k. Akshaya, “Sickle Cell Anemia Detection using Convolutional Neural Network,” 2021 12th Int. Conf. Comput. Commun. Net.Technol., IEEE Xplore, 2016, doi: 10.1109/ICCCNT51525.2021.9580165.

D. Pavithra and R.Vanithamani, “Chronic Kidney Disease Detection from Clinical Data using CNN,” IEEE Int. Conf.Distributed Computing,VLSI,Elect.Circ.Robo.,IEEE Xplore, 2021, doi: 10.1109/DISCOVER52564.2021.9663670.

M.D. John Paolo, B.D. John Michael, B.L. Noel and A.J. Roben, “Detection of Sickle Cell Anemia in Blood Smear using YOLOv3,” IEEE Int. Conf. Artificial. Int. Eng. Tech., EEE Xplore, 2022, doi: 10.1109/IICAIET55139.2022.9936781.

Wilson, T., Johnson, M., Gonzalez, L., Rodriguez, L., & Silva, A. Machine Learning Techniques for Engineering Workforce Management. Kuwait Journal of Machine Learning, 1(2). Retrieved from

K. Kousalya, B. Krishnakumar, R.S. Mohan and N.Karthikeyan , “Comparative Analysis of White Blood Cells Classification using Deep Learning Architectures,” 2nd Int. Conf. Smart. Electronics. Comm.,IEEE Xplore 2021, doi: 10.1109/ICOSEC51865.2021.9591771.

M.I.A. Ismail and A.D. Zaroug, “Blood Cells Classification using Deep Learning Technique,” Int. Conf. Eng. MIS., IEEE Xplore, 2022, doi: 10.1109/ICEMIS56295.2022.9914281.

Ahmed Ali, Anaïs Dupont,Deep Generative Models for Image Synthesis and Style Transfer , Machine Learning Applications Conference Proceedings, Vol 2 2022.

Jaya Prakash, S., Rani Chetty, M. S. ., & Jayalakshmi, A. . (2023). Image De-Hazing based on Krill Herd Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 310–315. Retrieved from

Ms. Elena Rosemaro. (2014). An Experimental Analysis Of Dependency On Automation And Management Skills. International Journal of New Practices in Management and Engineering, 3(01), 01 - 06. Retrieved from