Feast In: A Machine Learning Image Recognition Model of Recipe and Lifestyle Applications

Ann, Evelyn Toh Lee; Hao, Ng Sze; Wei, Goh Wei; Hee, Khor Chun

doi:10.1051/matecconf/202133504006

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Object detection is one of the most crucial research topics in the field of computer vision. Along with the machine learning [18][19][20] and deep learning [21,22] booms in image detection applications, several automated computer vision solutions have been introduced to assess image object detection. Before 2012, the traditional machine learning algorithm was generally adopted to conduct object detection.…”

Section: Related Workmentioning

confidence: 99%

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

Zhang

et al. 2022

Symmetry

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Computed tomography (CT) is the first modern slice-imaging modality. Recent years have witnessed its widespread application and improvement in detecting and diagnosing related lesions. Nonetheless, there are several difficulties in detecting lesions in CT images: (1) image quality degrades as the radiation dose is reduced to decrease radiational injury to the human body; (2) image quality is frequently hampered by noise interference; (3) because of the complicated circumstances of diseased tissue, lesion pictures typically show complex shapes; (4) the difference between the orientated object and the background is not discernible. This paper proposes a symmetry GAN detection network based on a one-stage detection network to tackle the challenges mentioned above. This paper employs the DeepLesion dataset, containing 10,594 CT scans (studies) of 4427 unique patients. The symmetry GANs proposed in this research consist of two distinct GAN models that serve different functions. A generative model is introduced ahead of the backbone to increase the input CT image series to address the typical problem of small sample size in medical datasets. Afterward, GAN models are added to the attention extraction module to generate attention masks. Furthermore, experimental data indicate that this strategy has significantly improved the model’s robustness. Eventually, the proposed method reaches 0.9720, 0.9858, and 0.9833 on P, R, and mAP, on the validation set. The experimental outcome shows that the suggested model outperforms other comparison models. In addition to this innovation, we are inspired by the innovation of the ResNet model in terms of network depth. Thus, we propose parallel multi-activation functions, an optimization method in the network width. It is theoretically proven that by adding coefficients to each base activation function and performing a softmax function on all coefficients, parallel multi-activation functions can express a single activation function, which is a unique ability compared to others. Ultimately, our model outperforms all comparison models in terms of P, R, and mAP, achieving 0.9737, 0.9845, and 0.9841. In addition, we encapsulate the model and build a related iOS application to make the model more applicable. The suggested model also won the second prize in the 2021 Chinese Collegiate Computing Competition.

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Section: Related Workmentioning

confidence: 99%

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

Zhang

et al. 2022

Symmetry

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“…Object detection is a critical research topic in the "science" of computer vision. With the growth of deep learning [20,21] and machine learning [22][23][24] in image detection applications, various sophisticated computer vision systems used for evaluating object detections have been presented. Up until 2012, the classic machine learning approach was commonly used to detect objects.…”

Section: Related Workmentioning

confidence: 99%

GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

Zhang

Liu

et al. 2022

Remote Sensing

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There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% mAP by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology.

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MISOhungry: A Platform that Encourages Easy Access to Recipe Search, Management, and Traditional Recipes

Thakur

Chandna

2022

Design, User Experience, and Usability: Design Thinking and Practice in Contemporary and Emerging Technologies

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Feast In: A Machine Learning Image Recognition Model of Recipe and Lifestyle Applications

Cited by 6 publications

References 13 publications

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

MISOhungry: A Platform that Encourages Easy Access to Recipe Search, Management, and Traditional Recipes

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