Abstract:
Leukemia is a difficult type of blood cancer which can present in a combination of multiple
types, that had individual cellular and genetic abnormalities. The prevalent forms of
leukemia include Acute lymphocytic leukemia (ALL), Acute myeloid leukemia (AML),
Chronic lymphocytic leukemia (CLL) and Chronic myeloid leukemia (CML). The
evaluation of diseases, risk detection, and treatment planning all depend on the
identification of specific genetic anomalies. This work describes a method for recovering
Leukemia blood cancer cells applying blood microscopic images, identifying the subtypes
of Leukemia. For detecting leukemia cancer using image processing techniques, that detect
classification of leukemia blood cell subtypes. The proposed approach involves utilizing
Convolutional Neural Network (CNN) to identify and categorize subtypes to leukemia
blood cells based on microscopic images of human blood cells. Aimed to assess the expert
Seven independent CNN models such as EfficientNetB7, ResNet-50, VGG19, ResNet101,
DenseNet201, MobileNet and also built a CML-1(custom model) detect in the
classification of leukemia. After exploit our proposed methodology, ResNet-50 model
exhibited superior performance achieving 97.50% accuracy. The remaining models also
displayed robust performance, with accuracy rates ranging from 95% to 97%. These
finding imply that employing CNN methodologies for the automated identification of
leukemia in microscopic blood images holds immense potential and presents substantial
benefits in the realm of medical diagnostics.
