Vision Transformer (ViT)-based Applications in Image Classification

Huo, Yingzi; Jin, Kai; Cai, Jiahong; Xiong, Huixuan; Pang, Jiacheng

doi:10.1109/bigdatasecurity-hpsc-ids58521.2023.00033

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…The ViT model primarily comprises three key modules: Linear Projection of Flattened Patches, Transformer Encoder, and MLP Head. ViT exhibits robust feature extraction capabilities and demonstrates resilience to various forms of noise and disturbances 33 . Pre‐trained ViT models on extensive datasets showcase remarkable generalization abilities.…”

Section: Methodsmentioning

confidence: 99%

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Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos

Liu,

Han,

Liang

et al. 2024

Int J Imaging Syst Tech

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Left ventricular hypertrophy (LVH) is a common clinical manifestation associated with cardiovascular adverse events. Relying solely on the subjective judgment, it is challenging to promptly and accurately diagnose mild LVH. Quantitatively measuring the interventricular septum and left ventricular posterior wall to diagnose mild LVH is a time‐consuming and labor‐intensive process that is prone to errors. To propose a novel method for rapid and automatic end‐to‐end diagnosis of mild LVH. We propose a novel end‐to‐end automated method for detecting mild LVH. This method achieves rapid end‐to‐end detection of mild LVH in echocardiographic videos without the need for quantitative measurements. Initially, representative frames are extracted from echocardiographic videos, and these frames are then automatically diagnosed by the proposed network to detect LVH. The network architecture primarily consists of three key components: a feature extractor, bidirectional LSTM, and attention module. The Vit‐b model achieved 88% video classification accuracy in the experiment where 32 frames are extracted, and the ViT‐l model achieves 92% video classification accuracy in the experiment where 16 frames are extracted. It is shown experimentally that extracting fewer video frames can diagnose LVH more accurately. The experiments illustrate the superior performance and competitiveness of this method compared to other approaches, potentially applicable to the clinic.

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Section: Methodsmentioning

confidence: 99%

“…ViT exhibits robust feature extraction capabilities and demonstrates resilience to various forms of noise and disturbances. 33 Pre-trained ViT models on extensive datasets showcase remarkable generalization abilities.…”

Section: Vision Transformermentioning

confidence: 99%

Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos

Liu,

Han,

Liang

et al. 2024

Int J Imaging Syst Tech

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CNN Transformer for the Automated Detection of Rheumatoid Arthritis in Hand Thermal Images

Ahalya,

Snekhalatha

2024

Lecture Notes in Computer Science

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A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Kandasamy,

Natarajan,

Sri Preethaa

et al. 2024

Neural Process Lett

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Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

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Vision Transformer (ViT)-based Applications in Image Classification

Cited by 10 publications

References 44 publications

Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos

Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos

CNN Transformer for the Automated Detection of Rheumatoid Arthritis in Hand Thermal Images

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

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