Identification of Leukemia Cancer Based on Deep Learning Techniques

Leukemia cancer has become the prime cause of cancer mortality throughout the board as the preliminary diagnosis can help to detect the ailment with sophisticated medical facilities. It is the type of cancer that originates from certain white blood cells which have they are forming. In leukemia, the stem cells in the bone marrow make lots of abnormal white blood cells they do not work properly, and this affects our immune system. Leukemia may also reduce the number of platelets and white blood cells. It mainly affects blood and forms bone marrow, Lymph nodes, and spleen. It is a type of malignant disorder that starts in the bone marrow, spreads to all body parts, and increases the amount of white blood cells; still, it is prone to challenging tasks in the medical image domain. This paper describes the Convolutional Neural Network (CNN) method to recognize chronic residents in MRI. Additionally, effective image segmentation methods are shown so that removal of the key attributes from the MRI pictures. Inceptionv3 is added to increase the accuracy by effectively enhancing the training model. On the ALL-IDB dataset, the suggested model is put to the test. To demonstrate the suggested model's effectiveness and accuracy, its results are contrasted with those of other cutting-edge methods. The suggested model's average performance on the dataset reaches 99.9% accuracy, 99.9% precision, and recalls 99.9%.