Automating Leukemia Diagnosis with Autoencoders: A Comparative Study

Minoo Sayyadpour, Nasibe Moghaddamniya, Touraj Banirostam
12/30/2023
cs.LGcs.AIcs.CY

No confident code implementation found for this paper.

Abstract

Leukemia is one of the most common and death-threatening types of cancer that threaten human life. Medical data from some of the patient's critical parameters contain valuable information hidden among these data. On this subject, deep learning can be used to extract this information. In this paper, AutoEncoders have been used to develop valuable features to help the precision of leukemia diagnosis. It has been attempted to get the best activation function and optimizer to use in AutoEncoder and designed the best architecture for this neural network. The proposed architecture is compared with this area's classical machine learning models. Our proposed method performs better than other machine learning in precision and f1-score metrics by more than 11%.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning

89% match2025

A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification

88% match2024

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

86% match2023

A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)

85% match2024

Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures

85% match2023

Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models

84% match2024

Cite this paper

@article{sayyadpour2023automating,
  title  = {Automating Leukemia Diagnosis with Autoencoders: A Comparative Study},
  author = {Minoo Sayyadpour and Nasibe Moghaddamniya and Touraj Banirostam},
  year   = {2023},
  eprint = {2401.00883},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.00883v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.00883.svg)](https://codeofpaper.com/paper/2401.00883)

Discussion