For the automatic diagnosis and classification of leukemia and leukemoid reactions, the IDB2 (acute lymphoblastic leukemia-image database) dataset has been utilized. This paper focuses on an automated method to differentiate between leukemoid and leukemia reactions using images of blood smear. MobileNetV3 is employed to classify and count WBC types from segmented images. The BCCD (Blood Cell Count Detection) dataset, which contains 364 images of blood smear and 349 single WBC type images, has been used in this work. The image segmentation algorithm incorporates Fuzzy C-means clustering, the snake algorithm, and fusion rules. For classification, the VGG19 CNN (Convolutional Neural Network) architecture-based deep learning technique is implemented. Python and Google colab is used for designing and executing the algorithm respectively.

Python Google Colab MobileNetV3 VGG 19 Fuzzy C-Means