A neural network based deep learning approach for efficient segmentation of brain tumor medical image data

Veeramuthu, A.; Swaminathan, M. S.; Kumar, K. Ashok

doi:10.3233/jifs-169980

Cited by 26 publications

(4 citation statements)

References 9 publications

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“…It is helpful to learn the features of various dementia causes. Unfortunately, a deep learning model has millions of parameters, and it takes a lot of time to train them before their use in production [21]. Moreover, to develop a strong deep learning neural network, it is necessary to collect a great deal of image data [22].…”

Section: B Prediction Of Dementia Risk Based On Machine Learningmentioning

confidence: 99%

Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization

2020

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With the development of healthcare technologies, the elderly population has grown and therefore populating ageing has emerged as a social issue. It is a cause of rise in patients with geriatric disorders, among which dementia is very fatal to the elderly's activities of daily living. In the studies on dementia risk prediction, a method using deep learning was proposed. It requires a lot of image data and much time to learn. Therefore, this study proposes a prediction model of dementia risk based on XGBoost using derived variable extraction from numericalized dementia data and hyper-parameters optimization. The proposed method extracts variable importance from typical independent variables with the use of gradient boosting and then generates derived variables. The generated derived variables are applied to variable importance analysis and thereby a Top-N group is created. Then, for achieving optimal performance in line with the data characteristics of each Top-N group, hyper-parameter tuning is conducted. With the optimized groups, XGBoost model based performance is evaluated. In addition, for the performance evaluation of the proposed model, goodness-of-fit for machine learning classification models is evaluated. According to the Top-N group performance evaluation with different numbers of derived variables, Top-20 model showed the best performance, and the optimized hyper-parameter values were eta=0.10, gamma=0, max_depth=4, and min_child_weight=1. As a result, the accuracy of the XGBoost model proposed in this study was 85.61%, and its F1-score was 79.28%. When the proposed model is compared with Decision Tree, Random Forest, SVM, and k-NN models, it has the best performance.

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Section: B Prediction Of Dementia Risk Based On Machine Learningmentioning

confidence: 99%

Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization

2020

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“…Traditional medical imaging image processing and analysis only rely on the doctor's experience, which not only wastes manpower but also affects the accuracy rate because the doctor's experience and physical condition affect judgment result. erefore, the breakthrough of automated medical image processing technology has a very critical role in improving the efficiency of medical diagnosis [2]. Medical image segmentation is an important task.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Technology of Nucleus Image Based on U-Net Network

Fang

Zhou

Wang

2021

Scientific Programming

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To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.

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“…The results of medical image segmentation usually have no intersection between each area, and each segmented area has a certain similarity in its interior. Therefore, the segmentation process needs to eliminate external interference factors to ensure the higher accuracy and reliability of the segmentation results [6] .…”

Section: Introductionmentioning

confidence: 99%

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

Zhang

Shi

Chen

et al. 2021

Preprint

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Aiming at the problems of rough edges and low accuracy in processing cell nucleus image segmentation in existing image segmentation methods. A cell nucleus image segmentation technology based on generative adversarial network (GAN) network and fully convolutional network (FCN) model is proposed. First, the FCN model is used to perform preliminary segmentation of the cell nucleus image, in which the fully connected layer convolution and skip connection are used to improve the accuracy of image segmentation. Then, improve the GAN network, introduce splitting branches into the discriminator structure, and combine the GAN network and the splitting network into one. At the same time, pixel loss is introduced in the generator to obtain a nucleus image that is visually more similar to the real image. Finally, the segmented image output by the FCN model is used as the input of the GAN network to achieve high-precision segmentation of the nucleus image. The proposed method is experimentally demonstrated based on the 2018 data science bowl dataset. The results show that it can achieve rapid convergence, and the mean intersection over union (MIoU) is 85.34%, which is better than other comparison methods.

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A neural network based deep learning approach for efficient segmentation of brain tumor medical image data

Cited by 26 publications

References 9 publications

Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization

Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization

Segmentation Technology of Nucleus Image Based on U-Net Network

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

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