Leukemia Disease Detection Using Machine Learning Algorithm
S Tharanipriya, Ajith Kumar R, K. Akash, Aswin S. Babu, Sanchala Bharat Sunilkumar · 2024
Both adults and children can be affected by leukemia, a type of malignant blood cell cancer. An automated approach to leukemia identification is offered in this endeavor. In a manual method of detecting leukemia, experts look at tiny pictures. This is an arduous and drawn-out process that lacks conventional accuracy and depends on individual skill. In order to get over these restrictions, the automated leukemia diagnosis approach examines the microscopic image. 60,140 people are expected to receive a leukemia diagnosis in 2016, according to the Leukemia and Lymphoma Society. An early diagnosis is necessary for the patient to receive the most effective treatment. An early diagnosis support system is therefore desperately needed to direct the treatment of patients with acute leukemia. A DenseNet-based method for differentiating between normal and pathological blood is proposed in this study. The proposed method outperforms previous methods in terms of accuracy.