Blood Cell Image Classification

In the realm of medical diagnostics, automated analysis of blood cell images has gained significant attention for its potential to enhance efficiency and accuracy. This study proposes a robust methodology employing Convolutional Neural Networks (CNNs) integrated with a Residual Network (ResNet) module for precise classification of blood cell types. Leveraging the power of deep learning, the proposed CNN-ResNet architecture aims to discern and classify various blood cell types, such as erythrocytes, leukocytes, and platelets, from microscopic images. The utilization of ResNet's residual learning mechanism intends to overcome the challenge of vanishing gradients while enabling the network to delve deeper into feature extraction. Through extensive experimentation and validation on diverse datasets, this approach demonstrates promising results, showcasing superior classification accuracy and robustness. The implications of this work extend to revolutionizing automated hematological analysis, potentially contributing to more efficient and reliable medical diagnostics in the domain of blood-related disorders and diseases.

 KEYWORDS: convolutional neural networks (CNNs), Resnet.