Acute Lymphoblastic Leukemia Detection and Classification Using an Ensemble of Classifiers and Pre-Trained Convolutional Neural Networks
Keywords:
Leukemia, Convolutional Neural Networks, Ensemble Learning, pre-trained models, Deep Learning, Cancer DetectionAbstract
Leukemia affects a significant portion of the global population. It is one of the most prevalent types of cancer among adults and children, according to the World Health Organization (WHO). The global incidence of leukemia has been increasing over the past few decades, due in part to an aging population and improved survival rates for other types of cancer. Early-stage leukemia detection at the lowest possible cost is a serious challenge in the field of leukemia disease diagnosis. Traditional methods such as blood tests, bone marrow tests, and spinal fluid tests are very time-consuming and have limited ability to analyze large amounts of data. Comparatively, the use of ensemble learning and pre-trained convolutional neural network (CNN) algorithms provide more accurate and efficient methods to detect and analyze the disease in less time. To improve the accuracy and optimize training time, we are proposing ensemble learning-based models for the detection of types of leukemia based on blood microscopic images, rather than traditional techniques. Ensemble learning can scan enormous amounts of data, including images, laboratory results, and patient information, to find patterns and predict the presence of the disease. This is one of the key benefits of adopting ensemble learning for leukemia identification. This can be especially helpful for examining small and complex changes in blood cell images, which are frequently challenging to spot using conventional techniques. Different pre-trained models are used in this work to identify different forms of leukemia. Pretrained networks such as ResNet50, VGG16, and InceptionV3 are relatively simple approaches for applying ensemble learning to image analysis. The three models—ResNet50, VGG16, and InceptionV3 —have been improved for feature extraction and classification. Experiments were carried out on the dataset, and an accuracy of about 90% was achieved.
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