Leukemia Detection Revolution: AI and Machine Learning Enhance Image-Based Diagnosis

Sheikh Sadi Bandan, MD. Samiul Islam Sabbir, Md Sharuf Hossain. and Khadiza Tul Kobra
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Sheikh Sadi Bandan: Dept. of Computer Science & Engineering Daffodil International University Dhaka, Bangladesh
MD. Samiul Islam Sabbir: Dept. of Computer Science & Engineering Daffodil International University Dhaka, Bangladesh
Md Sharuf Hossain.: Dept. of Data Science Loyola University Chicago, USA
Khadiza Tul Kobra: Dept. of Information Technology and Management Illinois Institute of Technology Chicago, USA

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 8, 632-641

Abstract: Currently, several diseases have become epidemic in Bangladesh, one of them is leukemia. This disease usually affects people of any age. It is usually found in blood cells, blood plasma, bone marrow. If this disease has been going on for a long time and without specific treatment, it often does not turn into cancer. It is called leukemia when it effects the blood and bone marrow. As a general rule white blood cells are affected by leukemia. Leukemia dataset is composed of both images of blood smears from leukemia patients and non-leukemia patients. While earlier research has simply found leukemia or categorized it into a few varieties, this study has recognized leukemia and defined its types, bringing the categorization process one step further. Artificial intelligence can help us with this. For instance, we can use machine learning and deep learning algorithms to detect leukemia cells and be alerted when they are detected. In this paper I have used the Customize CNN algorithm, through which we have used 7 architectures of CNN. The 5 methods of Customize CNN we used are VGG19, MobileNetV2, MobileNetV3, DenseNet201, Inception V3. The accuracy of VGG19 is 63%, that of MobileNetV2 is 97%, that of MobileNetV3 is 99%, that of DenseNet201 is 99%, that of Inception V3 is 96%. From all the methods, it can be seen that the highest accuracy has been found in MobileNetV3 and DenseNet201 methods. The lowest accuracy was obtained using the VGG19 model.

Date: 2024
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