Wei Kitt Wong, Melinda Melinda
In contemporary computer vision, convolutional neural networks (CNNs) and vision transformers (ViTs) represent the two primary architectural paradigms for image recognition. While both approaches have been widely adopted in medical imaging applications, they operate based on fundamentally different computational principles. This report attempts to provide brief application notes on ViTs and CNNs, particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations. Generally, CNNs rely on convolutional kernels, localized receptive fields, and weight sharing, enabling efficient hierarchical feature extraction. These properties contribute to strong performance in detecting spatially constrained patterns such as textures, edges, and anatomical boundaries, while maintaining relatively low computational requirements. ViTs, on the other hand, decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image. This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn. However, ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining, as their reduced inductive bias requires greater data exposure to learn robust representations. This report briefly examines the architectural structure, underlying mathematical foundations, and relative performance characteristics of CNNs and ViTs, drawing upon recent findings from contemporary research. Emphasis is placed on understanding how differences in data availability, computational resources, and task requirements influence model effectiveness across medical imaging domains. Most importantly, the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks. © 2026 By Author(s). Published by TMR Publishing Group Limited.
Department of Electrical and Computer Engineering, Curtin University Malaysia, CDT 250, Sarawak, Miri, 98009, Malaysia; Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
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