Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures
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...
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MDPI AG
2025-05-01
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| author | Hari Mohan Rai B. Omkar Lakshmi Jagan N. Thiruapthi Rao Thayyaba Khatoon Mohammed Neha Agarwal Hanaa A. Abdallah Saurabh Agarwal |
| author_facet | Hari Mohan Rai B. Omkar Lakshmi Jagan N. Thiruapthi Rao Thayyaba Khatoon Mohammed Neha Agarwal Hanaa A. Abdallah Saurabh Agarwal |
| author_sort | Hari Mohan Rai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a4cd5e586eeb46a0a950538b0bb3cdd5 |
| institution | OA Journals |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-a4cd5e586eeb46a0a950538b0bb3cdd52025-08-20T02:21:09ZengMDPI AGFractal and Fractional2504-31102025-05-019633710.3390/fractalfract9060337Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple ArchitecturesHari Mohan Rai0B. Omkar Lakshmi Jagan1N. Thiruapthi Rao2Thayyaba Khatoon Mohammed3Neha Agarwal4Hanaa A. Abdallah5Saurabh Agarwal6School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of KoreaDepartment of Electrical and Electronics Engineering, Vignan’s Institute of Information Technology (A), Duvvada, Visakhapatnam 530049, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Duvvada, Visakhapatnam 530049, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad 500090, Telangana, IndiaSchool of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaLeukemia 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.https://www.mdpi.com/2504-3110/9/6/337leukemia classificationdeep learning architecturesmedical image analysisweb-based diagnostic interface |
| spellingShingle | Hari Mohan Rai B. Omkar Lakshmi Jagan N. Thiruapthi Rao Thayyaba Khatoon Mohammed Neha Agarwal Hanaa A. Abdallah Saurabh Agarwal Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures Fractal and Fractional leukemia classification deep learning architectures medical image analysis web-based diagnostic interface |
| title | Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures |
| title_full | Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures |
| title_fullStr | Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures |
| title_full_unstemmed | Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures |
| title_short | Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures |
| title_sort | deep learning for leukemia classification performance analysis and challenges across multiple architectures |
| topic | leukemia classification deep learning architectures medical image analysis web-based diagnostic interface |
| url | https://www.mdpi.com/2504-3110/9/6/337 |
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