Hybrid Convolution neural Network and Graph Neural network for Detection of Leukocyte

Abstract

In pathology and medical diagnostics, the identification of leukocytes, white blood cells essential for immune system function, is significant. The hybrid neural network model used in this study, which combines the strengths of convolutional neural networks (CNNs) and graph neural networks (GNNs), gives a unique method for the identification of leukocytes. Due to the intricate spatial and relational patterns visible in microscopic pictures of blood samples, leukocyte identification is difficult. The proposed hybrid model incorporates a GNN module to record the contextual associations among leukocytes in a blood sample and a CNN module to extract fine-grained characteristics from individual leukocyte pictures. Each leukocyte picture is seen in this paradigm as a node in a network, with the edges denoting the interactions between the cells' locations or contexts. While the GNN module uses graph-based representations to capture the connections and dependencies between leukocytes within a blood sample, the CNN module efficiently extracts local visual characteristics from leukocyte pictures. These two modules are used to produce a thorough and context-sensitive depiction of the leukocyte detection issue. The importance of this study resides in its potential to increase the precision and effectiveness of leukocyte identification in pathology and medical diagnostics, leading to better patient care and illness diagnosis. This hybrid CNN-GNN model may also be used for a variety of other medical image analysis tasks that call for taking both local and contextual information into account. In successfully identifying leukocytes in microscopic images, this hybrid CNN-GNN technique shows promising results. Accuracy, precision, recall, and F1-score are common assessment metrics used to assess the model's performance.