A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization

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A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL) Jaya Basnet1 · Abeer Alsadoon1   · P. W. C. Prasad1 · Sarmad Al Aloussi2 · Omar Hisham Alsadoon3

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, interclass separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem. Keywords  White blood cells · Modified loss function · Regularization · Deep convolutional neural network · Intra-class compactness · Inter-class separability

* Abeer Alsadoon [email protected] Extended author information available on the last page of the article

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Abbreviations WBC White blood cells DCNN Deep convolutional neural network CNN Convolutional neural network NCC Normalized cross-correlation SCA Sine Cosine Algorithm ALL Acute Lymphocytic Leukaemia PCA Principal component analysis MCC Matthews correlation coefficient AUC​ Area under curve RNN Recurrent neural network LSTM Long short-term memory TLA Transfer Learning Algorithm FCL Fully connected layer NN Neural netwo