Volume 14 , Issue 2 , PP: 33-52, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
B. Divyapreethi 1 * , A. Mohanarathinam 2
Doi: https://doi.org/10.54216/JCIM.140203
Leukemia, a cancer that attacks human white blood cells, is one of the deadliest illnesses. Detecting affected cells in microscopic images becomes tedious because feature variants are not predicted correctly by a hematologist. Therefore image handling techniques failed to select the importance of the features scaling counts, entities, and precise size and shape of cells presented in the microscopic image. To resolve this problem, Deep Spectral Convolution Neural Network (DSCNN) based on Leukemia cancer detection using Invariant Entity Scalar Feature Selection (IESFS) is proposed to identify the risk factor of cancer for early diagnosis. Initially, preprocessing is carried out using cascade Gabor filters. Based on Structural Cascade Segmentation (SCS), the white blood cell regions are categorized into affected and non-affected margins and verify the edges using canny edge mapping. This estimates the scaling cell size, counts, entities and angular cell projection of weights from each segmented feature region. Then find the entity relation of cell projection equivalence using Color Intensive Histogram Equalization (CIHE). After segmenting the angular vector, projection scaling is applied to correlate the entity's object scaling comparator. Then scaling features were selected using Invariant Entity Scalar Feature Selection (IESFS) by averaging the mean depth values of feature weight and trained with a deep convolution neural network for predicting max equivalence entity weights for finding the affected cells and counts in microscopic images. This improves the prediction of cancer cell accuracy as well high performance in sensitivity 92.7 %, specificity 92.3 %, and f-measure 93.6 % with redundant time complexity.
Leukemia Cancer Detection: Feature selection and classification: Structural Cascade Segmentation: CNN: Histogram Equalization.
[1] D. Kumar et al., "Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 142521-142531, 2020, DOI: 10.1109/ACCESS.2020.3012292.
[2] S. O. Heriawati, T. Harsono, M. M. Bachtiar and Y. Hernaningsih, "Blood Cells Classification for Identification of Acute Lymphoblastic Leukemia on Microscopic Images Using Image Processing," 2021 International Electronics Symposium (IES), 2021, pp. 215-220, DOI: 10.1109/IES53407.2021.9593939.
[3] N. S. P. Kong, H. Ibrahim, C. H. Ooi and D. C. J. Chieh, "Enhancement of Microscopic Images Using Modified Self-Adaptive Plateau Histogram Equalization," 2009 International Conference on Computer Technology and Development, 2009, pp. 308-310, DOI: 10.1109/ICCTD.2009.46.
[4] H. Li, X. Zhao, A. Su, H. Zhang, J. Liu and G. Gu, "Color Space Transformation and Multi-Class Weighted Loss for Adhesive White Blood Cell Segmentation," in IEEE Access, vol. 8, pp. 24808-24818, 2020, DOI: 10.1109/ACCESS.2020.2970485.
[5] M. H. Waseem et al., "On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction," in IEEE Access, vol. 7, pp. 141072-141082, 2019, DOI: 10.1109/ACCESS.2019.2944295.
[6] Ambeth Kumar, V.D. (2017). Automation of Image Categorization with Most Relevant Negatives. Pattern Recognition and Image Analysis, 27(3), 371–379.
[7] Kumar, I., Kumar, A., Kumar, V.D.A. et al. (2022) Dense Tissue Pattern Characterization Using Deep Neural Network. Cogn Comput 14, 1728–1751.
[8] L. KG and N. Manja Naik, "Automated Detection of White Blood Cells Cancer Disease," 2019 1st International Conference on Advances in Information Technology (ICAIT), 2019, pp. 24-28, DOI: 10.1109/ICAIT47043.2019.8987352.
[9] R. P and S. D. P, "Detection of Blood Cancer-Leukemia using K-means Algorithm," 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 838-842, DOI: 10.1109/ICICCS51141.2021.9432244.
[10] Ambeth Kumar, S. Malathi ,R. Venkatesan, K Ramalakshmi, Weiping Ding, Abhishek Kumar. (2020). Exploration of an innovative geometric parameter based on performance enhancement for foot print recognition. Journal of Intelligent and Fuzzy System , 38 (2), 2181-2196.
[11] V. Acharya, V. Ravi, T. D. Pham and C. Chakraborty, "Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia," in IEEE Transactions on Engineering Management, DOI: 10.1109/TEM.2021.3103549.
[12] A. Soleimanian et al., "An Efficient Graphene Quantum Dots-Based Electrochemical Cytosensor for the Sensitive Recognition of CD123 in Acute Myeloid Leukemia Cells," in IEEE Sensors Journal, vol. 21, no. 15, pp. 16451-16463, 1 Aug.1, 2021, DOI: 10.1109/JSEN.2021.3079224.
[13] D. Kumar et al., "Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 142521-142531, 2020, DOI: 10.1109/ACCESS.2020.3012292.
[14] Kumar, V.D.A., Sharmila, S., Kumar, A. et al. (2023). A novel solution for finding postpartum haemorrhage using fuzzy neural techniques. Neural Comput & Applic. 35(33), 23683–23696
[15] L. Boldú, A. Merino, S. Alférez, A. Molina, A. Acevedo, and J. Rodellar, ‘‘Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis,’’ J. Clin. Pathol., vol. 72, no. 11, pp. 755–761, Nov. 2019.
[16] A. Shah, S. S. Naqvi, K. Naveed, N. Salem, M. A. U. Khan and K. S. Alimgeer, "Automated Diagnosis of Leukemia: A Comprehensive Review," in IEEE Access, vol. 9, pp. 132097-132124, 2021, DOI: 10.1109/ACCESS.2021.3114059.
[17] P. K. Das, S. Meher, R. Panda and A. Abraham, "An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders," in IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2021.3062152.
[18] D. Kumar et al., "Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 142521-142531, 2020, DOI: 10.1109/ACCESS.2020.3012292.
[19] Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. (2022). Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation. Symmetry, 14(12), 2512
[20] T. Dharani and S. Hariprasath, "Diagnosis of Leukemia and its types Using Digital Image Processing Techniques," 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018, pp. 275-279, DOI: 10.1109/CESYS.2018.8724075.
[21] Balakrishnan, Chitra, and V. D. Ambeth Kumar. (2023). IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics 13(4), 775
[23] S. Khan, M. Sajjad, T. Hussain, A. Ullah and A. S. Imran, "A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images," in IEEE Access, vol. 9, pp. 10657-10673, 2021, DOI: 10.1109/ACCESS.2020.3048172.
[24] J. Amin et al., "An Integrated Design based on Dual Thresholding and Features Optimization for White Blood Cells Detection," in IEEE Access, DOI: 10.1109/ACCESS.2021.3123256.
[25] H. N. Fakhouri and S. H. Al-Sharaeh, ''A hybrid methodology for automation the diagnosis of leukaemia based on quantitative and morphological feature analysis,'' Modern Appl. Sci., vol. 12, no. 3, p. 56, Feb. 2018.
[26] Piyush K. Pareek, Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning “,Fusion: Practice and Applications, Volume 2 , Issue 1 , PP: 42-60, 2020
[27] Shivam Grover, Kshitij Sidana, Vanita Jain, “Egocentric Performance Capture: A Review”, Fusion: Practice and Applications, Volume 2, Issue 2 , PP: 64-73, 2020.
[28] Abdel Nasser H. Zaied, Mahmoud Ismail and Salwa El- Sayed, A Survey on Meta-heuristic Algorithms for Global Optimization Problems, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 48-60, 2020
[29] Mahmoud H.Alnamoly, Ahmed M. Alzohairy, Ibrahim M. El-Henawy, “A survey on gel images analysis software tools, Journal of Intelligent Systems and Internet of Things,Volume 1 , Issue 1 , PP: 40-47, 2021.
[30] Sherubha, “Graph Based Event Measurement for Analyzing Distributed Anomalies in Sensor Networks”, Sådhanå(Springer), 45:212, https://doi.org/10.1007/s12046-020-01451-w
