Abstract: Leukemia is a condition that affects the bone marrow and/or blood and is related to white blood cells (WBC). The ability to diagnose leukaemia in its earliest stages quickly, safely, and accurately is crucial for both treating and preserving patients' lives. According to advances, there are two main types of leukaemia: acute and chronic. Blood cancer develops as a result of the unchecked proliferation of these white blood cells. The suggested work offers a reliable method for categorising Multiple Myeloma (MM) and Acute Lymphoblastic Leukemia (ALL) using the SN-AM dataset. The bone marrow overproduces lymphocytes in Acute Lymphoblastic Leukaemia (ALL) and Multiple Myeloma (MM) results in the accumulation of cancer cells in the bone marrow as opposed to their release into the bloodstream. The model pre-processes the images and then extracts the best features after being trained on cell images. The model is then trained using the RestNet50, and it concludes by predicting the type of cancer that is present in the cells. A Convolutional Neural Network (CNN) with 50 layers deep is called RestNet50.
Keywords: ResNet50, CNN, Acute Lymphoblastic Leukaemia, Multiple Myloma
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