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.
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