Home / Current Issue / Paper 1712680
Hybrid Ensemble Deep Learning Framework for Blood Cancer Identification
Subject area: Science,Engineering and Technology · Area of research: Medical Science
DOI: https://doi.org/10.64388/IREV9I6-1712680
Abstract
Leukemia is a life-threatening malignancy that originates in the blood-forming tissues and rapidly affects the production and morphology of white blood cells. Conventional diagnosis relies on manual inspection of peripheral blood smear (PBS) images by hematologists, a procedure that is time- consuming, subjective, and difficult to scale in real clinical settings. In this work, we present a Hybrid Ensemble Deep Learning framework that combines a transfer-learning based Convolutional Neural Network (CNN), a Multi-Layer Perceptron (MLP), and an Ensemble Voting Classifier to identify blood cancer from microscopic images. The proposed system integrates robust preprocessing, aggressive data augmentation, feature extraction using MobileNetV2, fully connected decision layers and both hard and soft voting schemes. Experiments on a publicly available Kaggle dataset of leukemic and normal smear images achieve an overall accuracy above 95%, with strong precision and F1-score across malignant classes. A graphical user interface implemented using Python Tkin- ter and a Flask web back-end demonstrate that the model can be deployed for real-time, image-based decision support in hospital environments.
Keywords
Blood Cancer; Leukemia; Deep Learning; Ensemble Clas- sifier; CNN; Medical Imaging; Hybrid Model
How to cite this paper
@article{1712680,
author = {Waseem Shareef K. S., Syed Sharfuddin Shuaib, Syed Umar, Syed Maaz Athar, Syeda Muzammil},
title = {Hybrid Ensemble Deep Learning Framework for Blood Cancer Identification},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {657-659},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712680.pdf},
abstract = {Leukemia is a life-threatening malignancy that originates in the blood-forming tissues and rapidly affects the production and morphology of white blood cells. Conventional diagnosis relies on manual inspection of peripheral blood smear (PBS) images by hematologists, a procedure that is time- consuming, subjective, and difficult to scale in real clinical settings. In this work, we present a Hybrid Ensemble Deep Learning framework that combines a transfer-learning based Convolutional Neural Network (CNN), a Multi-Layer Perceptron (MLP), and an Ensemble Voting Classifier to identify blood cancer from microscopic images. The proposed system integrates robust preprocessing, aggressive data augmentation, feature extraction using MobileNetV2, fully connected decision layers and both hard and soft voting schemes. Experiments on a publicly available Kaggle dataset of leukemic and normal smear images achieve an overall accuracy above 95%, with strong precision and F1-score across malignant classes. A graphical user interface implemented using Python Tkin- ter and a Flask web back-end demonstrate that the model can be deployed for real-time, image-based decision support in hospital environments.},
keywords = {Blood Cancer; Leukemia; Deep Learning; Ensemble Clas- sifier; CNN; Medical Imaging; Hybrid Model},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712680}
}