Modified <scp>U‐Net</scp> for cytological medical image segmentation

Benazzouz, Mourtada; Benomar, Mohammed Lamine; Moualek, Youcef

doi:10.1002/ima.22732

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…Not only is the number of normal X-ray images generally higher than those containing lesions, but due to factors such as the complex diversity of disease pathogenesis, there may be significant biases in the distribution of samples for certain diseases, with large disparities in the number of samples for different categories of disease. The problem of data imbalance is currently addressed at two main levels [9][10] .…”

Section: Uneven Distribution Of Case Datamentioning

confidence: 99%

“…Sensitivity, also known as true positive rate and recall, is used to measure the ability of the algorithm to discriminate between regions of lung nodules, i.e. the ratio of the number of correctly predicted samples among all nodule samples, with higher sensitivity representing a lower rate of missed detections by the algorithm, as defined by the formula in equation (9).…”

Section: Negative Fn Tnmentioning

confidence: 99%

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Deep learning in the design and application of thoracic medical image-assisted diagnosis algorithm

Xiao

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Section: Uneven Distribution Of Case Datamentioning

confidence: 99%

Section: Negative Fn Tnmentioning

confidence: 99%

Deep learning in the design and application of thoracic medical image-assisted diagnosis algorithm

Xiao

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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“…In the medical field, there are many medical imaging technologies, such as computed tomography (CT), magnetic resonance image (MRI), and ultrasound imaging, which are all applications of medical segmentation technology. Medical image segmentation 1,2 is to segment out the special meaningful parts of medical images and extract the corresponding features to provide a reliable basis for clinical diagnosis and pathology research, it is a key step in medical image processing, with the help of which doctors can make accurate judgments. The development of medical image segmentation methods has so far produced the formation of different segmentation algorithms, such as traditional methods based on thresholding, 3 edge detection‐based segmentation algorithms, 4 region‐based segmentation algorithms, 5 and active contour‐based segmentation algorithms, 6 which are better able to complete the segmentation task.…”

Section: Introductionmentioning

confidence: 99%

GR‐Net: Gated axial attention ResNest network for polyp segmentation

Jiang

Hua

2023

Int J Imaging Syst Tech

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Medical image segmentation is a key step in medical image analysis. The small differences in the background and foreground of medical images and the small size of most medical data sets make medical segmentation difficult. This paper uses a global‐local training strategy to train the network. In the global structure, ResNest is used as the backbone of the network, and parallel decoders are added to aggregate features, as well as gated axial attention to adapt to small datasets. In the local structure, the extraction of image details is accomplished by dividing the images into equal patches of the same size. To evaluate the performance of the model, qualitative and quantitative comparisons were performed on five datasets, Kvasir‐SEG, CVC‐ColonDB, CVC‐ClinicDB, CVC‐300, and ETIS‐LaribPolypDB, and the segmentation results were significantly better than the current mainstream polyp segmentation methods. The results show that the model has better segmentation performance and generalization ability.

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“…In recent years, medical image segmentation based on deep learning has been widely used due to the continuous development of artificial intelligence and computer vision [ 9 , 10 , 11 ]. By using deep neural network models and large amounts of medical image data for training and learning, this method achieves automated segmentation and annotation tasks on medical images and features high efficiency, accuracy, and stability.…”

Section: Introductionmentioning

confidence: 99%

LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation

Zhang,

Niu

2023

Bioengineering

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In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet’s structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision.

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Modified U‐Net for cytological medical image segmentation

Cited by 7 publications

References 37 publications

Deep learning in the design and application of thoracic medical image-assisted diagnosis algorithm

Deep learning in the design and application of thoracic medical image-assisted diagnosis algorithm

GR‐Net: Gated axial attention ResNest network for polyp segmentation

LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation

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