Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification

2025/08/04 by Faisal Ahmed, Ahmed, Faisal · 3 citations
Computer Science · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #F.2.2 #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.7 #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2508.06535

openalex publication_date 2025/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Accurate classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images is essential for early diagnosis and effective treatment planning. This study investigates the use of transfer learning with pretrained convolutional neural networks (CNNs) to improve diagnostic performance. To address the class imbalance in the dataset of 3,631 Hematologic and 7,644 ALL images, we applied extensive data augmentation techniques to create a balanced training set of 10,000 images per class. We evaluated several models, including ResNet50, ResNet101, and EfficientNet variants B0, B1, and B3. EfficientNet-B3 achieved the best results, with an F1-score of 94.30%, accuracy of 92.02%, andAUCof94.79%,outperformingpreviouslyreported methods in the C-NMCChallenge. Thesefindings demonstrate the effectiveness of combining data augmentation with advanced transfer learning models, particularly EfficientNet-B3, in developing accurate and robust diagnostic tools for hematologic malignancy detection.

Citations

• HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification
• Topological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classification
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer
• A Survey on Transfer Learning

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• HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis

Related

• Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
• A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)
• A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods
• Deep Learning with Self-Attention and Enhanced Preprocessing for Precise Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Smears in Hemato-Oncology
• Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks