Efficient Transfer Learning Approach for Acute Lymphoblastic Leukemia Diagnosis: Classification of Lymphocytes and Lymphoblastic Cells

Introduction: Acute lymphoblastic leukemia (ALL) is a severe illness that affects children and adults, and it can be fatal when left untreated.This leukemia strikes children and adolescents suddenly, often claiming their lives within just a few weeks after diagnosis.To diagnose ALL, hematologists investigate blood slides and bone marrow samples.Manual blood testing methods, which have been around for a long time, are typically laborious and may result in lower-quality diagnoses.ALL is essentially the unchecked growth of immature cells found in the bone marrow, often referred to as lymphoblasts.Methods: This research focuses on the classification of lymphoblast and lymphocyte cells using a computer-assisted method that employs deep learning and image processing techniques.This classification involves several steps.Prior to feature extraction, preprocessing and data augmentation are performed on the ALL-IBD dataset.Features are extracted from this augmented database using transfer learning with pre-trained networks (DenseNet121, ResNet50, InceptionV3, Xception).The selected and transformed features, obtained through principal component analysis (PCA), are then subjected to 5-fold cross-validation for hyper-tuning and training of individual machine learning models (LR, SVM, DT, RF).Finally, a soft voting classification model is proposed to predict lymphocytes and lymphoblasts.Results: The suggested ensemble method achieved 98.23% accuracy.SVM and the ensemble model with DenseNet121 and all feature sets reached an AUC of 1.00.LR achieved an AUC of 1.0 with all features and 0.99 with DenseNet121 features.The minimum AUC for DT was 0.64 and for RF was 0.86.AUC with all features was 0.80 for DT and 0.91 for RF.Conclusion: The suggested method uses image processing and deep learning to analyze blood cells automatically, avoiding the many limitations of manual analysis.The acquired results demonstrate that the presented approach may be employed as a diagnostic tool for ALL, which is undoubtedly helpful to pathologists.Observation: This procedure can also be employed for enumeration, as it offers exceptional efficiency and enables prompt suspicion of a diagnosis, which can subsequently be validated by a hematologist using specialized techniques.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.