COMPARISON OF DENSENET AND RESNET ARCHITECTURES FOR CLASSIFICATION ON WHITE BLOOD CELL
Abstract
White blood cell examination is useful in determining disease conditions. Accuracy in white blood cell classification is crucial, as errors can result in improper disease detection. Currently, white blood cell classification in healthcare is still done manually, so a system is needed to reduce classification errors. Researchers have tried to classify white blood cells using CNN, ResNet architecture provides good performance. Similar to ResNet, DenseNet also has "skip connections", but the difference is that it has fewer parameters. DenseNet has been evaluated on competitive datasets and the results outperformed ResNet. So in this study, we suspect that the DenseNet architecture can provide higher performance than ResNet in classifying white blood cells. We wanted to compare the performance of ResNet and DenseNet for classifying white blood cells. We used certain parameters and divided the dataset with various comparisons on the two architectures. Then the performance of the model will be evaluated by calculating sensitivity, specificity, and accuracy. Thus, the results of this study can be implemented in the health sector and can help health workers when classifying white blood cells with the right predictions. The best result from the comparison of both DenseNet and ResNet architectures is the DenseNet architecture. DenseNet obtained the results of sensitivity 64,85234678, specificity 88,26916351, and accuracy 82,40852433.
Keywords
Full Text:
PDFReferences
Md. A. R. Ridoy and Md. R. Islam, “A Lightweight Convolutional Neural Network for White Blood Cells Classification,” in 2020 23rd International Conference on Computer and Information Technology (ICCIT), DHAKA, Bangladesh: IEEE, Dec. 2020, pp. 1–5. doi: 10.1109/ICCIT51783.2020.9392649.
M. J. Macawile, V. V. Quinones, A. Ballado, J. D. Cruz, and M. V. Caya, “White blood cell classifi-cation and counting using convolutional neural network,” in 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), Nagoya: IEEE, Apr. 2018, pp. 259–263. doi: 10.1109/ICCRE.2018.8376476.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hono-lulu, HI: IEEE, Jul. 2017, pp. 2261–2269. doi: 10.1109/CVPR.2017.243.
S. Cheng, Y. Suhua, and J. Shaofeng, “Improved faster RCNN for white blood cells detection in blood smear image,” in 2019 14th IEEE International Conference on Electronic Measurement & In-struments (ICEMI), Changsha, China: IEEE, Nov. 2019, pp. 1677–1682. doi: 10.1109/ICEMI46757.2019.9101445.
A. Gautam and H. Bhadauria, “Classification of white blood cells based on morphological features,” in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India: IEEE, Sep. 2014, pp. 2363–2368. doi: 10.1109/ICACCI.2014.6968362.
T. Treebupachatsakul and S. Poomrittigul, “Bacteria Classification using Image Processing and Deep learning,” in 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), JeJu, Korea (South): IEEE, Jun. 2019, pp. 1–3. doi: 10.1109/ITC-CSCC.2019.8793320.
S. Manik, L. M. Saini, and N. Vadera, “Counting and classification of white blood cell using Artifi-cial Neural Network (ANN),” in 2016 IEEE 1st International Conference on Power Electronics, In-telligent Control and Energy Systems (ICPEICES), Delhi, India: IEEE, Jul. 2016, pp. 1–5. doi: 10.1109/ICPEICES.2016.7853644.
W. Yu et al., “Automatic classification of leukocytes using deep neural network,” in 2017 IEEE 12th International Conference on ASIC (ASICON), Guiyang: IEEE, Oct. 2017, pp. 1041–1044. doi: 10.1109/ASICON.2017.8252657.
T. Rosyadi, A. Arif, Nopriadi, B. Achmad, and Faridah, “Classification of leukocyte images using K-Means Clustering based on geometry features,” in 2016 6th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia: IEEE, Aug. 2016, pp. 245–249. doi: 10.1109/INAES.2016.7821942.
G. Liang, H. Hong, W. Xie, and L. Zheng, “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification,” IEEE Access, vol. 6, pp. 36188–36197, 2018, doi: 10.1109/ACCESS.2018.2846685.
A. Gautam, P. Singh, B. Raman, and H. Bhadauria, “Automatic classification of leukocytes using morphological features and Naïve Bayes classifier,” in 2016 IEEE Region 10 Conference (TEN-CON), Singapore: IEEE, Nov. 2016, pp. 1023–1027. doi: 10.1109/TENCON.2016.7848161.
A. U. R. Durrani, N. Minallah, N. Aziz, J. Frnda, W. Khan, and J. Nedoma, ‘Effect of hyper-parameters on the performance of ConvLSTM based deep neural network in crop classification’, PLoS ONE, vol. 18, no. 2, p. e0275653, Feb. 2023, doi: 10.1371/journal.pone.0275653.
S. Ozturk, U. Ozkaya, B. Akdemir, and L. Seyfi, ‘Convolution Kernel Size Effect on Convolu-tional Neural Network in Histopathological Image Processing Applications’, in 2018 International Symposium on Fundamentals of Electrical Engineering (ISFEE), Bucharest, Romania: IEEE, Nov. 2018, pp. 1–5. doi: 10.1109/ISFEE.2018.8742484.
S. Sharma, S. Sharma, and A. Athaiya, ‘ACTIVATION FUNCTIONS IN NEURAL NET-WORKS’, IJEAST, vol. 04, no. 12, pp. 310–316, May 2020, doi: 10.33564/IJEAST.2020.v04i12.054.
L. Datta, ‘A Survey on Activation Functions and their relation with Xavier and He Normal Ini-tialization’. arXiv, Mar. 18, 2020. Accessed: Nov. 30, 2023. [Online]. Available: http://arxiv.org/abs/2004.06632
A. Ramdan, V. Zilvan, E. Suryawati, H. F. Pardede, and V. P. Rahadi, ‘Tea clone classification using deep CNN with residual and densely connections’, Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 289–296, Oct. 2020, doi: 10.14710/jtsiskom.2020.13768.
B. M. Mathunjwa, Y.-T. Lin, C.-H. Lin, M. F. Abbod, and J.-S. Shieh, ‘ECG arrhythmia classi-fication by using a recurrence plot and convolutional neural network’, Biomedical Signal Processing and Control, vol. 64, p. 102262, Feb. 2021, doi: 10.1016/j.bspc.2020.102262.
X. Liang et al., ‘R-Drop: Regularized Dropout for Neural Networks’. arXiv, Oct. 29, 2021. Ac-cessed: Dec. 01, 2023. [Online]. Available: http://arxiv.org/abs/2106.14448
I. Kandel and M. Castelli, ‘The effect of batch size on the generalizability of the convolutional neu-ral networks on a histopathology dataset’, ICT Express, vol. 6, no. 4, pp. 312–315, Dec. 2020, doi: 10.1016/j.icte.2020.04.010
DOI: https://doi.org/10.24167/proxies.v8i1.12474
Copyright (c) 2024 Proxies : Jurnal Informatika