Issue |
E3S Web Conf.
Volume 472, 2024
International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2023)
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Article Number | 03013 | |
Number of page(s) | 13 | |
Section | Sustainable Development | |
DOI | https://doi.org/10.1051/e3sconf/202447203013 | |
Published online | 05 January 2024 |
Study of Machine Learning Algorithms on Early Detection of Leukemia
1 PG Scholar, Department of ECE, CVR College of Engineering, HYD, India
2 Assoc Prof, Department of ECE, CVR College of Engineering, HYD, India
1 ramesh.abhi506@gmail.com
2 swapnathouti@gmail.com
Leukemia is a type of cancer that impacts the blood cells and the bone marrow it can be classified into acute and chronic. Early detection is crucial for reducing mortality rates. Acute leukemia progresses rapidly, causing bleeding issues, infections, and anemia due to premature of white blood cells accumulating blood vessels in bone marrow. Chronic leukemia advances slower, leading to an aggregation of abnormal cells. Detecting leukemia more effectively and accurately, automated and machine learning algorithms are being developed. Training algorithms on extensive blood smear images datasets allow these methods to differentiate normal cells from abnormal ones. With faster and more standardized detection of leukemia, medical pathologist can make more informed decisions. While machine learning algorithms may improve detection, skilled healthcare professionals remain essential for interpreting results and providing optimal patient care. The proposed model give a demonstration of a convolutional neural network (CNN) and TensorFlow framework, this method predicts leukemia cells from healthy blood samples this technique has gained popularity as a valuable tool for diagnosing leukemia as well as treating its accuracy of 92.62%. The second approach is that to classify large dataset images of the malignant cells from the normal cells, we employ a VGG19, a ResNet50, and a ResNet101 neural network, as well as batch normalization of the images achieved better accuracy and F1-score.
Key words: Acute and Chronic leukemia / Convolutional neural network / Transfer Learning / Tensor Flow framework
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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