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Abunadi, I. and Senan, E.M. (2022) Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques. Sensors, 22, Article 1629.
https://doi.org/10.3390/s22041629

has been cited by the following article:

• TITLE: A Hybrid Deep and Machine Learning Framework with Feature Selection for Automated Classification of Acute Lymphoblastic Leukemia

  AUTHORS: Husne Farah, Fahmida Islam, Shuvo Biswas, Mohammad Shorif Uddin

  KEYWORDS: Blood Cancer, Acute Lymphoblastic Leukemia, Machine Learning, Deep Learning, Feature Selection

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.3, March 31, 2026

  ABSTRACT: Acute Lymphoblastic Leukemia (ALL), a highly aggressive subtype of blood cancer, demands early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods relying on manual microscopic analysis are frequently tedious and susceptible to human error. This study proposes a hybrid intelligent framework that combines deep learning (DL), machine learning (ML), and statistical feature selection to automate the classification of ALL from microscopic blood smear images. A publicly available dataset containing 3262 images is utilized. Deep learning models—VGG16, VGG19, ResNet50, and MobileNet—are used to extract high-level features, which are then fed into four ML classifiers: K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine (SVM). Among all configurations, the ResNet50 + SVM combination gave the top result of 99.54%. Further enhancement using Analysis of Variance (ANOVA) for feature selection increased the accuracy to 99.69%. The proposed hybrid approach demonstrates strong potential for clinical deployment as a reliable and efficient tool for automated leukemia diagnosis.