Medical image identification methods: A review

Li, Juan; Jiang, Pan; An, Qing; Wang, Gai-Ge; Kong, Hua-Feng

doi:10.1016/j.compbiomed.2023.107777

Cited by 3 publications

(3 citation statements)

References 151 publications

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“…Up sampling is the process of altering a signal to erroneously raise the sampling rate. Considering the limitations of 2D U-Net in capturing contextual information in 3D MRI brain tumor images, researchers proposed 3DU-Net ( Vanderbecq et al, 2020 ), significantly improving the segmentation performance of 2D network models ( Li et al, 2023 ). In addition, addressing issues such as insufficient high-resolution feature representation of small-scale and irregular brain tumor regions based on 3DU-Net ( Smith et al, 2023 ), researchers introduced autoencoders ( Yousefirizi et al, 2022 ), attention mechanisms ( Zhao et al, 2023 ), and cascade architectures into the network ( Niyas et al, 2022 ), encouraging research into 3DU-Net models for BTS ( Venkatesan et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, addressing issues such as insufficient high-resolution feature representation of small-scale and irregular brain tumor regions based on 3DU-Net ( Smith et al, 2023 ), researchers introduced autoencoders ( Yousefirizi et al, 2022 ), attention mechanisms ( Zhao et al, 2023 ), and cascade architectures into the network ( Niyas et al, 2022 ), encouraging research into 3DU-Net models for BTS ( Venkatesan et al, 2022 ). Myronenko ( Li et al, 2023 ) improved the accuracy of BTS by cascading a VAE into the 3DU-Net on the cascade network for BTS. When it came time for the 2018 BTS Challenge (BraTS), this approach took first place.…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

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According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

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“…With the development of medical imaging technology, it is easy to nondestructively visualize the intricate internal structures of organisms with enhanced precision and resolution [11][12][13]. In early studies, researchers manually determined the cross-sectional area of the pectoral muscle in ultrasound images and combined it with live weight to construct a regression equation for predicting the breast muscle weight of broilers [9,14,15].…”

Section: Introductionmentioning

confidence: 99%

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

Zhu,

Li,

Yang

et al. 2024

Animals

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Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

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NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

Bilal,

Liu,

Shafiq

et al. 2024

Computers in Biology and Medicine

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Medical image identification methods: A review

Cited by 3 publications

References 151 publications

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

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