A Generalized Pooling for Brain Tumor Segmentation

Hao, Kuankuan; Lin, Shukuan; Qiao, Jianzhong; Tu, Yue

doi:10.1109/access.2021.3130035

Cited by 15 publications

(11 citation statements)

References 30 publications

(36 reference statements)

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“…In addition, the improved log-cosh Dice loss and focal loss are combined so that the loss function can focus on both model classification ability and model segmentation ability. The loss function we propose is called focal Dice loss, as shown in formula (4). ω 1 and ω 2 are used to adjust the weights between the focal loss and the improved log-cosh Dice loss.…”

Section: Focal Dice Loss Functionmentioning

confidence: 99%

“…Accuracy is our most common evaluation metric, which refers to the ratio of the number of samples that are scored correctly to the number of all samples, and the higher the accuracy is, the better the model effect. (4) Focal − DiceLoss = ω 1 (FocalLoss)…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Therefore, how to efficiently diagnose brain tumour images and reduce image diagnostic error has become a research direction for many researchers. Currently, deep learning-based intelligent algorithms are widely used in brain tumour analysis tasks, and CNNs are adopted by researchers for their good segmentation performance and the convenience of feature extraction [4]. However, CNNs are prone to computational redundancy when processing a large number of dense images [5].…”

Section: Introductionmentioning

confidence: 99%

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Brain tumour segmentation based on an improved U-Net

2022

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Background Automatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours. Methods To solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding–decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics. Results The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions Our algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.

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Section: Focal Dice Loss Functionmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brain tumour segmentation based on an improved U-Net

2022

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“…Therefore, how to e ciently diagnose brain tumor images and reduce the diagnostic error of images has become a research direction for many researchers. Currently, deep learning-based intelligent algorithms are widely used in brain tumor analysis tasks, and CNNs are adopted by researchers for their good segmentation performance and the convenience of feature extraction [4]. However, CNN is prone to computational redundancy when processing a large number of dense images [5].…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation based on improved U-Net

Zheng

Zhu

Guo

2022

Preprint

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Background: Automatic segmentation of brain tumors using deep learning algorithms is one of the research hotspots in the field of medical image segmentation at this stage. An improved u-net network is proposed to segment brain tumors in order to improve the segmentation effect of brain tumors.Methods: In order to solve the problems that other brain tumor segmentation models such as U-Net have insufficient ability to segment edge details, poor extraction of location information and the commonly used Binary Cross-Entropy and Dice loss are often ineffective when used as loss functions for brain tumor segmentation models, we propose a serial encoding-decoding structure, which achieves improved segmentation performance by adding Hybrid Dilated Convolution (HDC) modules and the connections between each module of the two serial networks, in addition, we propose a new loss function in order to make the model more focused on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under IOU, PA, Dice, Precision, Hausdorf95, and ASD metrics.Results: The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of segmentation results shows that the segmentation results of our algorithm are closer to ground truth, showing more details of brain tumors, while the segmentation results of other algorithms are more smooth.Conclusions: Our algorithm has better semantic segmentation performance, compared with other commonly used segmentation algorithms. The technology we proposed can be used in the diagnosis of brain tumors to provide better protection for patients' later treatment.

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“…Most especially if the input feature is small, then it will result in some serious loss of features. So, in order to overcome this issue, right mix of these pooling layers which result in generalized pooling [12] will be the solution.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of AAD pooling unit and performance analysis

Kiran¹,

Rao²,

Sudheer³

et al. 2022

World J. Adv. Res. Rev.

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Convolutional Neural Network (CNN) has been witnessing a massive growth for its various applications in different fields. It is a category of Neural Network or Deep learning that is being used in text detection, image classification, etc. It is also very effective for the classifications which are non-image like audio classifications, signal data classifications, etc. CNN is composed of convolutional layer, pooling layer and finally the fully connected layer. In this work, we mainly focus on pooling layer which impacts accuracy and speed of CNN. This work implements validation of CNN based CIFAR-10 classifier and Field Programmable Gate Array (FPGA) implementation of Absolute Average Deviation (AAD) Pooling unit. Pooling techniques like max pooling, average pooling, mixed pooling, min pooling, etc., are currently being used. This work uses AAD pooling in CNN to support that this pooling is having higher accuracy, i.e., 89% and less computational complexity. A benchmark CNN structure using TensorFlow is adopted to quantify the performance of the AAD pooling unit. Further, Register Transfer Level (RTL) coding is done in Verilog HDL and the testbench is developed. The FPGA implementation is carried out using the Xilinx Vivado tool.

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A Generalized Pooling for Brain Tumor Segmentation

Cited by 15 publications

References 30 publications

Brain tumour segmentation based on an improved U-Net

Brain tumour segmentation based on an improved U-Net

Brain tumor segmentation based on improved U-Net

FPGA implementation of AAD pooling unit and performance analysis

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