Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network

被引:3
作者
Ananthakrishnan, Balasundaram [1 ,2 ]
Shaik, Ayesha [2 ]
Akhouri, Shivam [2 ]
Garg, Paras [2 ]
Gadag, Vaibhav [2 ]
Kavitha, Muthu Subash [3 ]
机构
[1] Vellore Inst Technol, Ctr Cyber Phys Syst, Chennai 600127, India
[2] Vellore Inst Technol, Sch Comp Sci & Engn, Chennai 600127, India
[3] Nagasaki Univ, Sch Informat & Data Sci, Nagasaki 8528521, Japan
关键词
Siamese network; deep learning; bone marrow; contrastive loss function; image convolution; SEGMENTATION;
D O I
10.3390/diagnostics13010112
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
The critical structure and nature of different bone marrow cells which form a base in the diagnosis of haematological ailments requires a high-grade classification which is a very prolonged approach and accounts for human error if performed manually, even by field experts. Therefore, the aim of this research is to automate the process to study and accurately classify the structure of bone marrow cells which will help in the diagnosis of haematological ailments at a much faster and better rate. Various machine learning algorithms and models, such as CNN + SVM, CNN + XGB Boost and Siamese network, were trained and tested across a dataset of 170,000 expert-annotated cell images from 945 patients' bone marrow smears with haematological disorders. The metrics used for evaluation of this research are accuracy of model, precision and recall of all the different classes of cells. Based on these performance metrics the CNN + SVM, CNN + XGB, resulted in 32%, 28% accuracy, respectively, and therefore these models were discarded. Siamese neural resulted in 91% accuracy and 84% validation accuracy. Moreover, the weighted average recall values of the Siamese neural network were 92% for training and 91% for validation. Hence, the final results are based on Siamese neural network model as it was outperforming all the other algorithms used in this research.
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页数:22
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