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Fractal Fract
Volume 9
Issue 6
10.3390/fractalfract9060337
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures

by
Hari Mohan Rai
1,†,
B. Omkar Lakshmi Jagan
2,
N. Thiruapthi Rao
3,
Thayyaba Khatoon Mohammed
4,†,
Neha Agarwal
5,†,
Hanaa A. Abdallah
6,* and
Saurabh Agarwal
7,*
1
School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
2
Department of Electrical and Electronics Engineering, Vignan’s Institute of Information Technology (A), Duvvada, Visakhapatnam 530049, Andhra Pradesh, India
3
Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Duvvada, Visakhapatnam 530049, Andhra Pradesh, India
4
Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad 500090, Telangana, India
5
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
6
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
7
School of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally as the first author.
Fractal Fract. 2025, 9(6), 337; https://doi.org/10.3390/fractalfract9060337
Submission received: 2 April 2025 / Revised: 15 May 2025 / Accepted: 18 May 2025 / Published: 23 May 2025
(This article belongs to the Special Issue Fractional Calculus Applications for Signal Processing and Data Science)
Versions Notes

Abstract

Leukemia is a very heterogeneous and complex blood cancer, which poses a significant challenge in its proper categorization and diagnosis. This paper aims to introduce various deep learning architectures, namely EfficientNet, LeNet, AlexNet, ResNet, VGG, and custom CNNs, for improved classification of leukemia subtypes. These models provide much improvement in feature extraction and learning, which further helps in the performance and reliability of classification. A web-based interface has also been provided through which a user can upload images and clinical data for analysis. The interface displays model predictions, symptom analysis, and accuracy metrics. Data collection, preprocessing, normalization, and scaling are part of the framework, considering leukemia cell images, genomic features, and clinical records. Using the preprocessed data, training is performed on the various models with thorough testing and validation to fine-tune the best-performing architecture. Among these, AlexNet gave a classification accuracy of 88.975%. These results strongly underscore the potential of advanced deep learning techniques to radically transform leukemia diagnosis and classification for precision medicine.
Keywords: leukemia classification; deep learning architectures; medical image analysis; web-based diagnostic interface leukemia classification; deep learning architectures; medical image analysis; web-based diagnostic interface

Share and Cite

MDPI and ACS Style

Rai, H.M.; Omkar Lakshmi Jagan, B.; Rao, N.T.; Mohammed, T.K.; Agarwal, N.; Abdallah, H.A.; Agarwal, S. Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures. Fractal Fract. 2025, 9, 337. https://doi.org/10.3390/fractalfract9060337

AMA Style

Rai HM, Omkar Lakshmi Jagan B, Rao NT, Mohammed TK, Agarwal N, Abdallah HA, Agarwal S. Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures. Fractal and Fractional. 2025; 9(6):337. https://doi.org/10.3390/fractalfract9060337

Chicago/Turabian Style

Rai, Hari Mohan, B. Omkar Lakshmi Jagan, N. Thiruapthi Rao, Thayyaba Khatoon Mohammed, Neha Agarwal, Hanaa A. Abdallah, and Saurabh Agarwal. 2025. "Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures" Fractal and Fractional 9, no. 6: 337. https://doi.org/10.3390/fractalfract9060337

APA Style

Rai, H. M., Omkar Lakshmi Jagan, B., Rao, N. T., Mohammed, T. K., Agarwal, N., Abdallah, H. A., & Agarwal, S. (2025). Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures. Fractal and Fractional, 9(6), 337. https://doi.org/10.3390/fractalfract9060337

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