USE OF ALEXNET ARCHITECTURE IN THE DETECTION OF BONE MARROW WHITE BLOOD CANCER CELLS
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Abstract
Purpose: To investigate the cancer affected area in the white blood cell images.
Methodology: In order to train and evaluate our CNN model, we implement a ten-fold cross-validation on the whole dataset, where 90% of the images are used for training and 10% are used for testing. The proposed system is implemented using CNN layer functions. The dataset is acquired from two different subsets of a dataset collection. The input to the model constitutes segmented cells of 227*227*3 images with zero center normalization.
Findings: 92.86% of accuracy has been achieved with 40 images by using convolutional neural network (CNN) of two layers. CNN can perform with more than 10,000 images. Based on the comparison analysis of this work with other works, CNN can produce the best results, if more number of images are used.
Originality/value: In this study, the pre-trained convolutional neural network with multiclass models have been modified for Support Vector Machine for classification of WBC into different categories for leukemia detection..
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Article Details
bone marrow; white blood cells; Alexnet; image processing; blood cancer
[2] K.Kousalya, S.Santhiya, K.Dinesh,”Blood cells classification using CNN architecture”, International Journal of Advanced Science & Technology, Vol.29, No.3, 2020. pp. 261-267.
[3] Dr. R. Janaki , ” Detection of Leukemia in Microscopic WBC images using Gaussian Feature Convolutional Visual Recognition Algorithm”, Journal of Critical Reviews, Vol 7, Issue 3, 2020.ISSN-2394-5125.
[4] H.Kutlu, E. Avci, F. Ozyurt,”WBC detection & Classification based on regional CNN”, Medical Hypotheses, 2020, 135, 109472.
[5] M. Sharma, A . Bhave, R . R Janghel ,” WBC classification using CNN”, In soft computing and Signal processing, 2019, pp.135-143, Springer, Singapore.