S-ResNet: An improved ResNet neural model capable of the identification of small insects

Wang, Pei; Luo, Fan; Wang, Lihong; Li, Chengsong; Niu, Qi; Li, Hui

doi:10.3389/fpls.2022.1066115

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…In order to improve the model's ability to recognize local details and extract features, we chose ResNet18 as the base model. The ResNet model can avoid gradient explosion caused by increased depth ( 14 ). In order to accelerate model training speed and avoid waste of computational resources, we introduced the idea of transfer learning ( 15 ) and pre-trained it using the classic image dataset ImageNet ( 16 ).…”

Section: Methodsmentioning

confidence: 99%

CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease

Zhixin,

Gang,

Zhixian

et al. 2024

Front. Cardiovasc. Med.

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Congenital heart disease is a prevalent birth defect, accounting for approximately one-third of major birth defects. The challenge lies in early detection, especially in underdeveloped medical regions where a shortage of specialized physicians often leads to oversight. While standardized chest x-rays can assist in diagnosis and treatment, their effectiveness is limited by subtle cardiac manifestations. However, the emergence of deep learning in computer vision has paved the way for detecting subtle changes in chest x-rays, such as lung vessel density, enabling the detection of congenital heart disease in children. This highlights the need for further investigation. The lack of expert-annotated, high-quality medical image datasets hinders the progress of medical image artificial intelligence. In response, we have released a dataset containing 828 DICOM chest x-ray files from children with diagnosed congenital heart disease, alongside corresponding cardiac ultrasound reports. This dataset emphasizes complex structural characteristics, facilitating the transition from machine learning to machine teaching in deep learning. To ascertain the dataset's applicability, we trained a preliminary model and achieved an area under the receiver operating characteristic curve (ROC 0.85). We provide detailed introductions and publicly available datasets at: https://www.kaggle.com/competitions/congenital-heart-disease.

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Section: Methodsmentioning

confidence: 99%

CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease

Zhixin,

Gang,

Zhixian

et al. 2024

Front. Cardiovasc. Med.

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“…This study incorporated additional well-established deep learning classification models, namely ResNet50 ( Targ et al, 2016 ) and EfficientNet-B4 ( Tan and Le, 2019 ). These models principally comprise convolutional layers, pooling layers, and fully connected layers.…”

Section: Methodsmentioning

confidence: 99%

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images

Zhu,

Liu,

et al. 2024

Front. Neurosci.

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AimConventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.MethodsThis research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.ResultsThe ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%.ConclusionAmong various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.

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“…These issues are not related to overfitting and must be carefully addressed during model development [ 35 ].The introduction of ResNet has significantly improved this challenge. ResNet is designed to preserve information integrity and simplify the learning objectives and difficulty through the use of skip connections with residual mapping [ 36 ] Additionally, ResNet is able to increase accuracy through increased depth [ 37 ]. In ResNet, the internal residual modules utilize shortcut connections, which allow the direct transfer of the input X to the output.…”

Section: Methodsmentioning

confidence: 99%

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

Yang

Cheng

et al. 2023

Molecules

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Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500–3000 cm−1, 3000–2750 cm−1, 1750–1100 cm−1 and 1100–400 cm−1, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750–1100 cm−1 to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.

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S-ResNet: An improved ResNet neural model capable of the identification of small insects

Cited by 15 publications

References 34 publications

CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease

CHD-CXR: a de-identified publicly available dataset of chest x-ray for congenital heart disease

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

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