Open Access

ARTICLE

Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

by G. Arutperumjothi^1,*, K. Suganya Devi², C. Rani³, P. Srinivasan⁴

1 Anna University, Tamil Nadu, India
2 Department of CSE, National Institute of Technology, SilcharCachar, Assam, India
3 Department of CSE, Government College of Engineering, Salem, Tamilnadu, India
4 Department of Physics, National Institute of Technology, SilcharCachar, Assam, India

* Corresponding Author: G. Arutperumjothi. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 1069-1086. https://doi.org/10.32604/iasc.2023.028423

Received 09 February 2022; Accepted 15 March 2022; Issue published 06 June 2022

Abstract

In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil, eosinophil, lymphocyte, monocyte, neutrophil, erythroblast, platelet, myocyte, promyocyte and metamyocyte. The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing, feature extraction and classification. Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization (HE). It is enclosed to improve an image contrast. In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means (FCM) model whereas its centroid point optimality method with Slap Swarm based optimization strategy. Moreover some specific set of Gray Level Co-occurrence Matrix (GLCM) features of the segmented images are extracted to augment the performance of proposed detection algorithm. Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features. Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.

---

Keywords

---