Classification of Acute Lymphoblastic Leukemia Using MobileNet and EfficientNetB3
Veera Brahmaiah Oguluri, Mallela Siva Naga Raju, Jahnavi Vaka, Varshini Murukutla · 2023
Computer vision and deep learning are widely used in health care. Acute Lymphoblastic Leukaemia (ALL) is a severe form of malignancy that affects the body's white blood cells. An early and precise diagnosis of ALL is critical for successful therapy and improved patient outcomes. In this project, the effectiveness of deep learning techniques including MobileNet and EfficientNetB3 are explored in conjunction with data augmentation for the classification of Acute Lymphoblastic Leukemia (ALL) using C-NMC Leukemia Classification Dataset. The approach of this project involves generating augmented data using various techniques such as flipping, rotating, and zooming to remove data imbalance in a dataset. After that split the data into training and testing data and then fine-tune the MobileNet and EfficientNetB3 models on the trained data, and evaluate the performance of both models using testing data based on recall, accuracy, F1score, precision, and Area Under Curve.