Enhanced Detection of Acute Lymphocytic Leukemia Using Deep Learning and Hybrid Classifiers on Microscopic Blood Images
H. A. El Shenbary, Amr Tarek Elsayed, Khaled A. A. Khalaf Allah, Belal Z. Hassan · International Journal of Advanced Computer Science and Applications · 2025
There is no doubt that a significant number of individuals worldwide suffer from blood cancer. A lot of people are unaware of the dangers associated with this disease, which can be fatal. When diagnosed, patients may feel intense fear and a sense of powerlessness. In addition, due to the rarity of these diseases, patients often struggle to find the necessary help and information. A specific type of blood cancer called acute lymphocytic leukemia (ALL) mainly affects white blood cells and is particularly prevalent in children. Early detection of this disease will improve the chances of recovery. Therefore, it is crucial to have an accurate and dependable method for identifying blood cancers. Deep learning (DL) architectures have garnered significant interest within the computer vision realm. Recently, there has been a strong focus on the accomplishments of pretrained architectures in accurately describing or classifying data from various real-world image datasets. Classification performances of the proposed models are investigated by using Soft-max, Support Vector Machine (SVM), and K-Nearest Neighbors algorithm (K-NN) separately on a deep learning neural network (Alexnet and VGG19) to differentiate between the three types of ALL using microscopic images dataset. The experimental results demonstrate that the combination of Alexnet with SVM achieves outstanding classification performance on the leukemia dataset, particularly on the original(unsegmented) data, achieved 97.03%on bengin class, 96.14% on early class, 99.49% on pre class and 99.9% on pro class. This approach achieves higher accuracy levels than practicing physicians.