International Conference on Computer and Knowledge Engineering

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Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer

Authors :

Javad Mirzapour Kaleybar¹ Hooman Saadat² Hooman Khaloo³

1- Department of Computer Engineering, University College of Nabi Akram, Tabriz, Iran 2- Department of Electrical Engineering, Qazvin Branch, Iran 3- School of Technology Sharif University, Tehran, Iran

Keywords :

Medical Image Analysis،Transformer،Convolutional Neural Network،Deep Learning

Abstract :

This research paper addresses the challenges associated with traffic sign detection in self-driving vehicles and driver assistance systems. The development of reliable and highly accurate algorithms is crucial for the widespread adoption of traffic sign recognition and detection (TSRD) in diverse real-life scenarios. However, this task is complicated by suboptimal traffic images affected by factors such as camera movement, adverse weather conditions, and inadequate lighting. This study specifically focuses on traffic sign detection methods and introduces the application of the Transformer model, particularly the Vision Transformer variants, to tackle this task. The Transformer's attention mechanism, originally designed for natural language processing, offers improved parallel efficiency. Vision Transformers have demonstrated success in various domains, including autonomous driving, object detection, healthcare, and defense-related applications. To enhance the efficiency of the Transformer model, the research proposes a novel strategy that integrates a locality inductive bias and a transformer module. This includes the introduction of the Efficient Convolution Block and the Local Transformer Block, which effectively capture short-term and long-term dependency information, thereby improving both detection speed and accuracy. Experimental evaluations demonstrate the significant advancements achieved by this approach, particularly when applied to the German Traffic Sign Detection dataset.