mlsanityData to 14 Oct 2026

Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification

arXiv 2508.06535 ↗First posted 4 Aug 2025eess.IV cs.CV cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

6 of 24 references.

References · 6

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  3. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 221
  4. 2025Topological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classificationcited by 10
  5. 2025HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classificationcited by 10
  6. 2018LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancercited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosiscited by 9
  2. 2025RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classificationcited by 7
  3. 2025Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learningcited by 6
  4. 2026Four-Stage Alzheimer's Disease Classification from MRI Using Topological Feature Extraction, Feature Selection, and Ensemble Learningcited by 5
  5. 2026Hybrid Topological and Deep Feature Fusion for Accurate MRI-Based Alzheimer's Disease Severity Classificationcited by 4
  6. 2026Brain Tumor Classification from 3D MRI Using Persistent Homology and Betti Features: A Topological Data Analysis Approach on BraTS2020cited by 2
  7. 2026Enhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Datasetcited by 0
  8. 2026Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classificationcited by 0