DeepLymphoDetect: Leveraging Deep Learning Techniques for Acute Lymphoblastic Leukemia Detection in Blood Cells
Kameshwaran Senthil, R Monikaa, M Gopinath, S Vishwa, Yuktha Varshika J, S. Vanitha Sivagami · 2024
Leukemia is a cancer that affects the bone marrow and blood, causing an excess of aberrant white blood cells (WBC). It can be affected by various factors such as Genetic mutations, Chemical exposure, and Immune system disorders. One kind of leukemia is acute lymphoblastic leukemia (ALL) that rapidly progressing and potentially life-threatening hematologic malignancy that predominantly affects children. This research employs a Convolutional Neural Network (CNN) architecture to propose an automated method for diagnosing and detecting acute lymphoblastic leukemia (ALL). For this research, Lymphocyte images are used as the input. The quantity of input samples is increased using a conventional data augmentation methodology, and correct findings are obtained by using a cross-validation method. This research achieves a 98.32% accuracy, a precision of 98.76%, and 97.38% recall for detection and classification tasks. This research exceeds the performance metrics of other cutting-edge methods.