Abstract :

Blood cell analysis plays a vital role in the diagnosis of various diseases such as anemia, leukemia, and infections. Manual identification of blood cell types through microscopic examination is time-consuming, subjective, and prone to human error. To overcome these limitations, this work proposes an automated blood cell type detection system using deep learning techniques. The proposed system focuses on classifying major blood cell types such as red blood cells (RBCs), white blood cells (WBCs), and platelets from microscopic blood smear images. Convolutional Neural Networks (CNNs) are employed to automatically extract discriminative features from input images without manual feature engineering. Image preprocessing techniques such as noise removal, normalization, and data augmentation are applied to improve classification accuracy. The trained deep learning model learns complex patterns related to shape, texture, and color variations of blood cells. Experimental results demonstrate tha