Abstract
Leukemia is a type of cancer that occurs when abnormal blood cells take place in the bone marrow. Leukemia can either be acute (fastly growing) or chronic (slowly growing) and it is considered as one of the most commonly diagnosed cancers for children younger than the age of 15 or adults older than the age of 55. Leukemia can be diagnosed through various types of tests and depending on the aggressiveness of the disease, the treatment may differ. To provide a low-cost, time-efficient solution, this study employs the deep learning technique to train the Xception, VGG16, VGG19, and MobileNet models to optimize the accuracy of medical image detection. Through medical imaging, the trained model is able to detect anomalies in the dataset and identify whether the given data is a benign acute lymphoblastic leukemia (ALL) or a Pro-B ALL. Overall, this VGG16 showed the most optimal performance in terms of accuracy and precision, producing a 98.5% accuracy in detecting abnormal regions from the dataset. This study also further used XAI technique and a deep convolutional neural network to visualize the results of anomalies. As a result, this paper concluded that both deep learning and machine learning techniques are yet to replace human resources and intelligence as the heatmap and the LIME portrayal identified different regions as abnormal parts, therefore proving the inconsistency of deep learning technology.
Highlights
Leukemia is a cancer of blood cells, generally of the white blood cells (WBCs)
This paper aims to explain the use of deep learning technology in training the medical imaging technique to identify whether the given patient’s image shows benign acute lymphoblastic leukemia (ALL) or Pro-B ALL
This study used a dataset of 3256 peripheral blood smear (PBS) images from 89 patients suspected of acute lymphoblastic leukemia (ALL)
Summary
White blood cells play a crucial role in the human body by protecting it from invasions by abnormal cells, viruses, fungi, bacteria, and any other foreign substances. A majority of the white blood cells are produced in the bone marrow and likewise, leukemia patients have abnormal cells growing in the bone marrow of bones. Leukemia cannot be fully diagnosed through physical exams, patients most commonly discover and confirm their disease through imaging tests, blood tests, bone marrow biopsies, and aspiration. Patients go through one or more types of treatment: chemotherapy, radiation therapy, targeted therapy, biological or immunotherapy, or stem cell transplantation [1]. Deep learning technology has been used in the medical field for numerous purposes as chatbots and, most commonly, as medical imaging solutions to identify patterns of symptoms and specific types of diseases. A comparison chart of estimated cases and estimated deaths of Leukemia in the United States in 2021 [2]
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