Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework
DOI:
https://doi.org/10.25195/ijci.v50i2.502Keywords:
Acute lymphoblastic leukemia, Convolutional Autoencoder, Convolutional Neural Network, Feature extraction, Computer-aided diagnosisAbstract
Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. The design method employed deep neural networks to recognize the features of the cells in question and then distinguish them as either leukemic or healthy cell types. The proposed laboratory method, with the use of the curated datasets of annotated pathological images of normal lymphoid progenitor cells, aimed to tackle the challenges related to the lack of curated datasets with annotated images of these cells. These challenges are believed to be linked to imprecise and time-consuming leukemia diagnosis and cure process. The simulated results confirmed the efficiency of the suggested technique, where CAE showed a correlation coefficient of 0.987 for lymphoblastic cells and CNN had an accuracy rate of 99.92% in ALL diagnosis. Such data demonstrated the capability of deep-based methodologies to fight leukemia.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Iraqi Journal for Computers and Informatics

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
IJCI applies the Creative Commons Attribution (CC BY) license to articles. The author of the submitted paper for publication by IJCI has the CC BY license. Under this Open Access license, the author gives an agreement to any author to reuse the article in whole or part for any purpose, even for commercial purposes. Anyone may copy, distribute, or reuse the content as long as the author and source are properly cited. This facility helps in re-use and ensures that journal content is available for the needs of research.
If the manuscript contains photos, images, figures, tables, audio files, videos, etc., that the author or the co-authors do not own, IJCI will require the author to provide the journal with proof that the owner of that content has given the author written permission to use it, and the owner has approved that the CC BY license being applied to content. IJCI provides a form that the author can use to ask for permission from the owner. If the author does not have owner permission, IJCI will ask the author to remove that content and/or replace it with other content that the author owns or has such permission to use.
Many authors assume that if they previously published a paper through another publisher, they have the right to reuse that content in their PLOS paper, but that is not necessarily the case – it depends on the license that covers the other paper. The author must ascertain the rights he/she has of a specific license (a license that enables the author to use the content). The author must obtain written permission from the publisher to use the content in the IJCI paper. The author should not include any content in her/his IJCI paper without having the right to use it, and always give proper attribution.
The accompanying submitted data should be stated with licensing policies, the policies should not be more restrictive than CC BY.
IJCI has the right to remove photos, captures, images, figures, tables, illustrations, audio, and video files, from a paper before or after publication, if these contents were included in the author's paper without permission from the owner of the content.