Detection of Lymphoblastic Leukemia Using VGG19 Model

Acute Lymphoblastic Leukemia (ALL) and - Acute Myelogenous Leukemia (AML) is a terminal blood cell cancer that takes birth due to the uncontrolled growth of white blood cells which results in deathif not diagnosed in earlier stages. To diagnose leukemia, oncologists perform multiple tests on bone marrow and white blood cells. Manually diagnosing this disease is sometimes leads to less accuracy due to human error and expecting expert suggestions and supervision for diagnosing the cancer is a time-consuming process that leads the patient into life-threatening stages. To get a precise diagnosis, this research work proposes deep learning and image processing techniques. There are few research studies on Acute Lymphoblastic Leukaemia (ALL) that have been tested and achieved good results even though it is not precisely predicted and not in practical usage. This research paper proposes further improvements in classification and an accurate CNN model helps to predict and classify the type of Leukaemia. This research work experiments the VGG19 model architecture by using the ImageNet dataset.