A New Model for Blood Cancer Classification Based on Deep Learning Techniques
Hagar Ibrahim Mohamed, Fahad Kamal Elsheref, Shrouk Reda Kamal
Abstract
Artificial intelligence and deep learning algorithms have become essential fields in medical science. These algorithms help doctors detect diseases early, reduce the incidence of errors, and decrease the time required for disease diagnosis, thereby saving human lives. Deep learning models are widely used in Computer-Aided Diagnosis Systems (CAD) for the classification of various diseases, including blood cancer. Early diagnosis of blood cancer is crucial for effective treatment and saving patients' lives. Therefore, this study developed two distinct models to classify eight types of blood cancer. These types include follicular lymphoma (FL), mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and the subtypes of acute lymphoblastic leukemia (ALL) known as early pre-B, pre-B, pro-B ALL, and benign. AML and ALL are specific classifications for human leukemia cancer, while FL, MCL, and CLL are specific classifications for lymphoma. Both models consist of different phases, including data collection, preprocessing, feature extraction techniques, and the classification process. The techniques applied in these phases are the same in both proposed models, except for the classification phase. The first model utilizes the VGG16 architecture, while the second model utilizes DenseNet-121. The results indicated that DenseNet-121 achieved a lower accuracy compared to VGG16. VGG16 exhibited excellent results, achieving an accuracy of 98.2% when classifying the eight classes. This outcome suggests that VGG16 is the most effective classifier for the utilized dataset.