Quantifying imbalanced classification methods for leukemia detection

This codebase is part of supplementary materials of our paper "Quantifying imbalanced classification methods for leukemia detection"

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Authors:

Deponker Sarker Depto, Md. Mashfiq Rizvee, Aimon Rahman, Hasib Zunair, M Sohel Rahman and M.R.C. Mahdy

Short Description:

Leukemia is a cancer of the body's blood-forming tissues, including the bone marrow and the lymphatic system. There's a good chance of survival if leukemia is detected early. But imbalance is a crucial issue in medical imaging and diagonosis results of leukemia by CAD systems becomes often skewed. Through our effort we have tried to alleviate the imbalance class problem for leukemia detection.

Figure: We have presented classified and misclassified samples and their corresponding superimposed feature maps from best models to input images.

Published Paper Link

Quantifying imbalanced classification methods for leukemia detection

Citation

@article{depto2023quantifying,
title={Quantifying imbalanced classification methods for leukemia detection},
author={Depto, Deponker Sarker and Rizvee, Md Mashfiq and Rahman, Aimon and Zunair, Hasib and Rahman, M Sohel and Mahdy, MRC},
journal={Computers in Biology and Medicine},
volume={152},
pages={106372},
year={2023},
publisher={Elsevier}
}

DOI

https://doi.org/10.1016/j.compbiomed.2022.106372

Requirments

This work requirs

• Python: 3.8.1
• Tensorflow: 2.3.0
• Keras: 2.4.0

Imbalance Scanario

We provide bar-chart depiction of imbalanced scanarios

Results

We report the best scores from our implemented models

License

Distributed under the MIT License.