Hybrid Deep Learning for Blood Cancer Detection: A CNN-GCN Approach for Enhanced Diagnostic Accuracy

In medicine, rapid and accurate diagnosis of blood cancer is extremely important. To achieve this goal, a novel hybrid deep learning architecture based on the combination of Convolutional Neural Networks and Graph Convolutional Networks for microscopic blood images classification into categories containing normal vs cancerous is proposed. Using the feature extraction ability of CNNs and GCNs relational learning strengths, the model can extract both local features and more complex relationships within the image data. Performance tests: The designed hybrid model was tested on 6,220 microscopic blood images and outperformed state-of-the-art models like CNNs VGG16, DenseNet, and EfficientNet-B0, as it was almost 89% accurate. Of course, the introduction of GCN layers dramatically improves the performance of the model, equipping it with the ability to identify those complex patterns characteristic for cancerous cells. Indeed, the article quite honestly demonstrates how hybrid models appear to be helpful for medical image analysis: an opportunity has finally been created for early, accurate diagnostics of blood cancer.