Deploying deep convolutional neural network to the battle against cancer: Towards flexible healthcare systems

Shahin, Mohammad; Chen, F. Frank; Hosseinzadeh, Ali; Maghanaki, Mazdak

doi:10.1016/j.imu.2024.101494

Cited by 3 publications

(1 citation statement)

References 84 publications

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“…Over the last decade, DL models, particularly CNNs, have gained prominence in computer vision due to technological advances in processing and storage [64,65]. CNNs are crucial for tasks like image classification and recognition, utilizing a feed-forward topology [66][67][68][69].…”

Section: Crack Detection Using Deep Learning Methodsmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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