Open Access

ARTICLE

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Ghada Emam Atteia*
Information Technology Department, College of Computer & Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11461, Saudi Arabia
* Corresponding Authors: Ghada Emam Atteia. Emails: ,

Computer Systems Science and Engineering 2023, 45(1), 361-376. https://doi.org/10.32604/csse.2023.029597

Received 07 March 2022; Accepted 21 April 2022; Issue published 16 August 2022

Abstract

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

Keywords

Autoencoder; deep learning; CNN; leukemia; diagnosis; computer-aided diagnosis

Cite This Article

G. E. Atteia, "Latent space representational learning of deep features for acute lymphoblastic leukemia diagnosis," Computer Systems Science and Engineering, vol. 45, no.1, pp. 361–376, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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