Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods

Authors

  • A L Leena Jenifer Rajalakshmi Engineering College
  • B K Indumathi Rajalakshmi Engineering College
  • C P Mahalakshmi Rajalakshmi Engineering College

DOI:

https://doi.org/10.4108/eetpht.10.5269

Keywords:

ReLU, CNN, Sigmoid activation layer, Image classification

Abstract

INTRODUCTION: Deep learning uses multi-layer neural networks where the algorithm decides for itself whether aspects are essential for analysis based on the raw input. In general, deep learning networks get better as more data is used to train them. For a variety of applications, convolutional neural networks are frequently used to analyse, categorize, and detect images.

OBJECTIVES: The proposed system technique is used for automated analysis of malaria-detecting frameworks. A female Anopheles mosquito bite is the primary method of transmission of the blood disease malaria. It is still common to manually count and identify parasitized cells during microscopic examination of either thick or thin layers of haemoglobin, which takes time for disease prognosis.

METHODS: The current research uses a neural network based on convolution to catalogue images of cells with and without malaria infection. This method improves the precision of classification for the datasets under study. The ReLu activation function used by this model enables it to learn more quickly and perform more effectively.

RESULTS: The prediction of infected and healthy cells was done accurately by the proposed model, which uses only 3 layers of convolution, and this was the idea behind the implementation. The model achieved an improved accuracy of 99.77% across 12 iterations (epochs).

CONCLUSION: The proposed model is straightforward and successful in differentiating between malaria-infected and uninfected cells.

Downloads

Download data is not yet available.

References

Skaramagkas, V, Pentari, A, Kefalopoulou, Z, Tsiknakis, M. Multi-modal Deep Learning Diagnosis of Parkinson’s Disease-A Systematic Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023; 31: pp. 2399 - 2423. DOI: https://doi.org/10.1109/TNSRE.2023.3277749

Tripathi S. Image classification using small convolutional neural network. In: Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence); 10-11 January 2019; Noida, India. IEEE Xplore: IEEE; 2019. p. 483-487. DOI: https://doi.org/10.1109/CONFLUENCE.2019.8776982

Sriram, G.K. Deep Learning Approaches for Pneumonia Classification in Healthcare. In: Proceedings of the 3rd International Conference on Innovative Practices in Technology and Management; 22-24 February 2023; Uttar Pradesh, India. IEEE Xplore : IEEE; 2023. p.1-6.

Devi Padma, K. Exploring The Potential of Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study. In: Proceedings of the 4th International Conference on Intelligent Engineering and Management. 9-11 May 2023; London, United Kingdom; IEEE Xplore; 2023. p. 1-6.

Poostchi, M, Silamut, K, Maude, R.J, Jaeger, S. Image analysis and machine learning for detecting malaria. Translational Research. 2018; 194: pp.36-55. DOI: https://doi.org/10.1016/j.trsl.2017.12.004

Pattanaik, P,A, Mittal, M, and Khan, M,Z. Unsupervised deep learning cad scheme for the detection of malaria in blood smear microscopic images. IEEE Access. 2020; 8: pp.94936-94946. DOI: https://doi.org/10.1109/ACCESS.2020.2996022

Lin G, Shen W. Research on convolutional neural network based on improved ReLU piecewise activation function. Procedia computer science. 2018; 131: pp. 977-84. DOI: https://doi.org/10.1016/j.procs.2018.04.239

He, K. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 27-30 June 2016; Las Vegas, NV, USA. IEEE Xplore: IEEE; 2016. p. 770-778. DOI: https://doi.org/10.1109/CVPR.2016.90

Liang, Z. CNN-based image analysis for malaria diagnosis. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. 15-18 December 2016; Shenzhen, China; IEEE Xplore: IEEE; 2016. p. 493-496.

Jaspreet Singh, C, Abhishek, S, Karan, S, Rekha, R. Malaria Cell Image Classification using Deep Learning. International Journal of Recent Technology and Engineering. 2020; 8: pp. 1-7.

Patil, GG. Techniques of Deep Learning for Image Recognition. In: Proceedings of IEEE 5th International Conference for Convergence in Technology; 29-31 March 2019; Bombay , India. IEEE Xplore: IEEE; 2019; p. 1-5 . DOI: https://doi.org/10.1109/I2CT45611.2019.9033628

Deng, J. Imagenet: A large-scale hierarchical image database. In: Proceedings of IEEE conference on computer vision and pattern recognition; 20-25 June 2009; Miami, FL, USA. IEEE Xplore: IEEE; 2009. p. 248-255. DOI: https://doi.org/10.1109/CVPR.2009.5206848

Rajaraman, S, Antani, S.K, Poostchi, M, Silamut, K. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ. 2018; 6: pp. 1-17. DOI: https://doi.org/10.7717/peerj.4568

Qiumei, Z, Dan, T, Fenghua, W. Improved convolutional neural network based on fast exponentially linear unit activation function. IEEE Access. 2019; 7: pp.151359-151367. DOI: https://doi.org/10.1109/ACCESS.2019.2948112

Hassairi, S. Supervised image classification using deep convolutional wavelets network. Proceedings of International Conference on Tools with Artificial Intelligence; 09-11 November 2015; Vietri sul Mare, Italy. IEEE Xplore: IEEE; 2015. p. 265-271. DOI: https://doi.org/10.1109/ICTAI.2015.49

Vijay, K. Survey on chaos RNN–A root cause analysis and anomaly detection. In: V.D. Ambeth Kumar, editor. AIP Conference Proceedings. Proceedings of the 5th International Conference on Intelligent Computing ( IConIC 2k22); 25–26 March 2022; Chennai, India. AIP Publishing; 2023. p. 1-8.

Tek, F.B, Dempster, A.G, and Kale, I.: Malaria parasite detection in peripheral blood images. BMVA.2006; 1: pp. 347-356. DOI: https://doi.org/10.5244/C.20.36

Babu, R, Jayashree, K, Viswanathan, K,B ,Vijay K. An Efficient Spam Detector Model for Accurate Categorization of Spam Tweets Using Quantum Chaotic Optimization-Based Stacked Recurrent Network. Nonlinear Dynamics. 2023; 111: pp. 18523–40. DOI: https://doi.org/10.1007/s11071-023-08697-z

Iliev, A, Kyurkchiev, N, Markov S. On the approximation of the step function by some sigmoid functions. Journal of Mathematics & Computers in Simulation. 2017;133:pp 223-234. DOI: https://doi.org/10.1016/j.matcom.2015.11.005

Ross, N, E, Pritchard, C, J, Rubin, D, M, Duse, A, G. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing. 2006; 44: pp 427–436. DOI: https://doi.org/10.1007/s11517-006-0044-2

Das, D, K, Ghosh, M, Pal, M, Maiti, A, K, Chakraborty, C. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron. 2013; 45: pp 97–106. DOI: https://doi.org/10.1016/j.micron.2012.11.002

Devi, S.S, Sheikh, S.A, Talukdar, A, Laskar, R.H. Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear. Indian Journal of Science and Technology. 2016; 9: pp. 1-10. DOI: https://doi.org/10.17485/ijst/2016/v9i45/94119

Liang, Z. CNN ‐based image analysis for malaria diagnosis. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 15-18 December 2016; Shenzhen, China. IEEE Xplore: IEEE; 2016. p. 493–496. DOI: https://doi.org/10.1109/BIBM.2016.7822567

Bibin, D, Nair, M.S, Punitha, P. Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE Access. 2017; 5:pp. 9099–9108. DOI: https://doi.org/10.1109/ACCESS.2017.2705642

Gopakumar, G,P, Swetha, M, Sai Siva, G, SaiSubrahmanyam, G,R,K. Convolutional Neural Network based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner. Journal of Biophotonics. 2018; 11: pp. 111- 123. DOI: https://doi.org/10.1002/jbio.201700003

Rajaraman, S, Jaeger, S, Antani, S.K. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin‐blood smear images. PeerJ. 2019;7: pp. 1-16. DOI: https://doi.org/10.7717/peerj.6977

Fuhad, K,M, Tuba, JF, Sarker, M,R, Momen S, Mohammed, N, Rahman, T.: Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application. Diagnostics. 2020; 10: pp. 329- 348. DOI: https://doi.org/10.3390/diagnostics10050329

Braveen, M, Nachiyappan, S, Seetha, R, Anusha, K, ALBAE feature extraction based lung pneumonia and cancer classification. Soft Computing. 2023; 27: pp. 1-14. DOI: https://doi.org/10.1007/s00500-023-08453-w

Jayachitra, S. Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application. In: Nilay Khare, editor. Machine Learning, Image Processing, Network Security and Data sciences. Proceedings of the 4th International Conference on Machine Learning, Image Processing, Network Security and Data 21 Dec 2022; Springer Link: Springer Nature Switzerland; 2023. p. 27-38. DOI: https://doi.org/10.1007/978-3-031-24352-3_3

Sekar, J, Aruchamy, P. An Efficient Clinical Support System For Heart Disease Prediction Using TANFIS Classifier, Computational Intelligence. 2022; 38: pp. 610-640. DOI: https://doi.org/10.1111/coin.12487

Downloads

Published

01-03-2024

How to Cite

1.
Leena Jenifer AL, Indumathi BK, Mahalakshmi CP. Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 1 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5269