Citation :

Tanvi Dhole, Suprabha Devane, Sanchi Jadhav, Trupti Jadhav, Prachi Pramod Waghmare, "Hybrid Deep Learning Framework for Histopathology Image Classification of Lung and Colon Cancers Using ResNet18, ViT, GCN, and ViT+GAT," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 5, pp. 1-9, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I5P101

Abstract

Cancer causes a large number of deaths around the world every year. To diagnose cancers correctly, doctors examine tissue images carefully, but doing this manually takes a lot of time, and different doctors can reach different conclusions from the same image. This study presents a deep learning model that combines several techniques, such as ResNet18, Vision Transformer (ViT), Graph Convolutional Network (GCN), and Graph Attention Network (GAT), to classify these cancer images more accurately. ResNet18 is used to capture detailed local features from the images, while ViT analyzes the global context by understanding how different parts of the image relate to each other. GCN and GAT further model and refine the structural relationships between features. The novelty of this work lies in integrating convolutional, transformer-based, and graph-based learning within a single framework to jointly capture local, global, and relational information for histopathological image classification. Experimental evaluation on the LC25000 dataset demonstrates that the proposed ViT+GAT architecture achieves improved classification accuracy and generalization performance compared to standalone ResNet18, ViT, and GCN models. These results indicate that the proposed approach can support more reliable and efficient automated cancer diagnosis in computational pathology.

Keywords

Lung Cancer, Colon Cancer, Vi-sion Transformer (ViT), Graph Convolutional Network (GCN), Graph Attention Network (GAT), Histopathology Classification.

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