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Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM
Author(s) -
Khan Muhammad Attique,
Qasim Muhammad,
Lodhi Hafiz Muhammad Junaid,
Nazir Muhammad,
Javed Kashif,
Rubab Saddaf,
Din Ahmad,
Habib Usman
Publication year - 2021
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23578
Subject(s) - feature selection , support vector machine , pattern recognition (psychology) , extreme learning machine , artificial intelligence , computer science , hematopathology , feature (linguistics) , biology , artificial neural network , biochemistry , linguistics , philosophy , cytogenetics , chromosome , gene
In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.