Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features

Figures & data

Figure 1. Proposed system architecture for leukemia detection.

RFE – recursive feature elimination; MI – mutual information; mRmR – minimum recursive maximal relevance

Figure 2. Architecture of U-Net for segmentation of WBC nucleus.

Figure 3. Typical microscopic images: (a) healthy cells and (b) blast cells.

Figure 4. Segmentation of nucleus by U-Net: (a) healthy cells and (b) blast cells.

Figure 5. Performance of U-Net based segmentation.

Figure 6. Heat map of fused features: (a) healthy cells and (b) blast cells.

Figure 7. Heat map representation of selected features for healthy cells and blast cells: (a) MI (mutual information), (b) mRmR (minimum recursive maximal relevance) and (c) RFE (recursive feature elimination) based methods.

Figure 8. Heat map of 50% intersected features: (a) healthy cells and (b) blast cells.

Table 1. Significance of features using ANOVA test

Percentage of intersected deep features (Between treatments)	p-value
	AlexNet	GoogleNet	SqueezeNet	Proposed method
10%	0.0669	0.3217	0.6412	0.0231
20%	0.4212	0.0622	0.3823	0.0024
30%	0.1660	0.0454	0.0421	0.0011
50%	0.0445	0.0428	0.0344	*0.00001
70%	0.0410	0.0325	0.0282	0.0003
100%	0.0232	0.0213	0.0013	0.0001

*p-value – highly statistically significant

Table 2. Performance analysis of the proposed system with other recent deep learning networks

Methods	Precision (%)	Recall (%)	Specificity (%)	Accuracy (%)	F1-score
VGG 16	96.6	92.3	96.3	94.2	0.944
VGG19	97.1	93.6	96.9	95.1	0.957
AlexNet	97.8	94.9	97.6	96.2	0.967
GoogleNet	97.9	95.4	97.8	96.5	0.955
Squeezenet	97.1	93.6	96.9	95.1	0.957
Proposed method	99.2	98.1	99.1	98.2	0.963

Table 3. Performance comparison of the proposed method with other existing methods using the ALL–IDB2 dataset

Related works	Methodology	Accuracy (%)
Mishra et al. (2017)	GLCM + random forest	96
Mishra, Majhi, and Sa (2018)	GLRL + SVM	96.97
Abdeldaim et al. (2018)	Shape, color and texture + SVM(RBF)	93.41
Luis Vogado et al. (2018)	VGG 16 + SVM	96.20
Rehman et al. (2018)	CNN	97.78
Thanh et al. (2018)	CNN	96.60
Sahlol, Abdeldaim, and Hassanien (2019)	Shape, color and texture+ KNN	92.30
Shafique et al. (2019)	Alexnet	96.50
Das et al. (2020)	GLRLM + SVM(RBF)	96
Mohammed and Abdulla (2021)	Shape, color and texture features + VM	97.45
Genovese et al. (2021)	VAR-PCANet	97.62
Mondal et al. (2021)	Xception	93.90
Pradeep Kumar Das et al. (2021)	MobilenetV2 + ResNet18	97.18
Proposed Method	AlexNet + GoogleNet + SqueezeNet (Fused features) + SVM	98.20

Figure 9. Variation in computational complexity vs different epochs for considered methods.

Mishra, S., B. Majhi Sa, P. K. Sa, and L. Sharma. 2017. Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control 33:272–80. doi:https://doi.org/10.1016/j.bspc.2016.11.021.

 Web of Science ®Google Scholar

Mishra, S., B. Majhi, and P. K. Sa. 2018. GLRLM-Based feature extraction for Acute Lymphoblastic Leukemia (ALL) detection. Recent findings in intelligent computing techniques. Advances in Intelligent Systems and Computing 708. Springer, Singapore. doi:https://doi.org/10.1007/978-981-10-8636-6_41.

 Google Scholar

Abdeldaim, A., A. Talaat, M. Elhoseny, and A. E. Hassanien. 2018. Computer-Aided acute lymphoblastic leukemia diagnosis system based on image analysis. Advances in Soft Computing and Machine Learning in Image Processing 131–47. Springer. doi:https://doi.org/10.1007/978-3-319-63754-9_7.

 Google Scholar

Luis Vogado, H. S., M. S. Rodrigo Veras, H. D. Flavio Araujo, R. V. R. Silva, and R. T. K. Aires. 2018. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence 72:415–22. doi:https://doi.org/10.1016/j.engappai.2018.04.024.

 Web of Science ®Google Scholar

Rehman, A., N. Abbas, T. Saba, S. I. U. Rahman, Z. Mehmood, and H. Kolivand. 2018. Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Techniques 81 (11):1310–17. doi:https://doi.org/10.1002/jemt.23139.

 PubMed Web of Science ®Google Scholar

Thanh, T. T. P., C. Vununu, S. Atoev, S. H. Lee, and K. R. Kwon. 2018. Leukemia blood cell image classification using convolutional neural network. Computational Theory 10:54–58. doi:https://doi.org/10.7763/1198.

 Google Scholar

Sahlol, A. T., A. M. Abdeldaim, and A. E. Hassanien. 2019. Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Computing 23 (15):6345–60. doi:https://doi.org/10.1007/s00500-018-3288-5.

 Web of Science ®Google Scholar

Shafique, S., S. Tehsin, S. Anas, and F. Masud. 2019. Computer-assisted acute lymphoblastic leukemia detection and diagnosis. Second International conference on communication, computing and digital systems (C-CODE). Islamabad, Pakistan. 184–89. IEEE. doi:https://doi.org/10.1109/C-CODE.2019.8680972.

 Google Scholar

Das, P. K., P. Jadoun, and S. Meher. 2020. Detection and classification of acute lymphocytic leukemia. IEEE-HYDCON1–5. doi: https://doi.org/10.1109/HYDCON48903.2020.9242745.

 Google Scholar

Mohammed, Z. F., and A. A. Abdulla. 2021. An efficient CAD system for ALL cell identification from microscopic blood images. Multimedia Tools and Applications 80:6355–6368. doi:https://doi.org/10.1007/s11042-020-10066-6.

 Web of Science ®Google Scholar

Genovese, A., M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti. 2021. Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and deep learning. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1205–09. doi:https://doi.org/10.1109/ICASSP39728.2021.9414362.

 Google Scholar

Mondal, C., K. Hasan, T. Jawad, A. Dutta, R. Islam, A. Awal, and M. Ahmad. 2021. Acute Lymphoblastic Leukemia detection from microscopic images using weighted ensemble of convolutional neural networks. Journal of Image and Video Processing 1–10. doi:https://doi.org/10.20944/preprints202105.0429.v1.

 Google Scholar

Das, P. K., S. Meher, R. Panda, and A. Abraham. 2021. An efficient blood-cell segmentation for the detection of hematological disorders. IEEE Transactions on Cybernetics 1–12. doi:https://doi.org/10.1109/TCYB.2021.3062152.

 Web of Science ®Google Scholar