Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

Soomro, Toufique Ahmed; Zheng, Lihong; Afifi, Ahmed J.; Ali, Ahmed; Soomro, Shafiullah; Yin, Ming; Gao, Junbin

doi:10.1109/rbme.2022.3185292

Cited by 109 publications

(48 citation statements)

References 128 publications

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“…Its basic idea is to use a continuous curve to express the edge of the target, and to define an energy functional so that its independent variables include the edge curve, so the segmentation process is transformed into the process of solving the minimum value of the energy functional, which can be realized by solving the Euler.Lagrange equation corresponding to the function. The position of the curve where the energy reaches the minimum is the outline of the target [9].…”

Section: The Energy Functional-based Segmentationmentioning

confidence: 99%

Fw-U-Net: a new satellite image segmentation of forest and water based on U-Net structure

Wang

Huang

Liu

2022

Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022)

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As global environmental problems are becoming more and more serious, satellite image segmentation and automatic monitoring of forests and water bodies are becoming more and more important in order to prevent natural disasters and monitor natural conditions, including monitoring deforestation and areas affected by floods. In this paper, the traditional U-Net model is improved and a FW-U-Net model suitable for forest water segmentation is proposed. After verification, the segmentation verification scores of the forest coverage area and water area performance test set are 84.37%(83.65%) and 87.92%(85.42%) respectively, which has high accuracy and reference value for forest and water satellite image segmentation.

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Section: The Energy Functional-based Segmentationmentioning

confidence: 99%

Fw-U-Net: a new satellite image segmentation of forest and water based on U-Net structure

Wang

Huang

Liu

2022

Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022)

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“…With the continuous development of information technology, various deep learning models have been proposed and widely used for image segmentation [5]. The AlexNet model was proposed by Krizhevsky et al [6].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of White Blood Cells Based on CBAM-DC-UNet

Zhang

Yin

et al. 2023

IEEE Access

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Monitoring the morphology of blood leukocytes, plays an important role in medical research, especially in the treatment of diseases such as immunodeficiency. Traditional manual detection methods are susceptible to numerous interference factors. Therefore, blood cells are often segmented using deep-learning algorithms. This study proposes a U-Net model based on a combination of an attention mechanism and dilated convolutions. First, the traditional convolution in a double convolutional module in a network is replaced by dilated convolution, and multi-scale features are obtained by expanding the receptive field. Second, after each convolution layer in the upsampling layer, an attention mechanism module is combined to refine the adaptive features and improve the segmentation performance of the model. Finally, the RAdam optimizer was used to enhance the robustness of the learning rate variations. Through the ablation experiment of the three improvement directions, it was concluded that all three improvement directions had a positive effect on the segmentation result, and the improvement was the most effective when the three improvements were combined. The experimental results show that compared with the original U-Net model, the segmentation indicators of blood leukocytes, intersection over union (IOU), recall and accuracy were increased by 5.1%, 5.7% and 1.2%, respectively, which more accurately segmented blood leukocytes, which may be used for a greater degree of auxiliary leukocyte detection in the application of immunodeficiency and other diseases.

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“…The proposed model comprises several layers, such as convolution, LReLU, and batch normalization (BN). In contrast to earlier approaches, the proposed methodology does not include feature extraction and the selection or segmentation in the pre-processing stage [ 10 , 11 ], which needs prior feature extraction or segmentation of tumors from the MRI scans. The proposed model employs filter-based feature extraction, which can be useful in achieving high detection performance.…”

Section: Introductionmentioning

confidence: 99%

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

Ullah

Khan

et al. 2022

Sensors

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Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

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Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

Cited by 109 publications

References 128 publications

Fw-U-Net: a new satellite image segmentation of forest and water based on U-Net structure

Fw-U-Net: a new satellite image segmentation of forest and water based on U-Net structure

Segmentation of White Blood Cells Based on CBAM-DC-UNet

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

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