Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Jwaid, Wasan M.; Al-Husseini, Zainab Shaker Matar; Sabry, Ahmad H.

doi:10.15587/1729-4061.2021.238957

Cited by 12 publications

(9 citation statements)

References 18 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From a comparison between the developed method and recent studies such as the studies [17,22] that used Cystoscopic images with U-Net CNN, and the study [19] that used MRI images with transfer deep learning, the developed Inception-v3 architecture achieved better accuracy. Furthermore, it is found that the AUC for the true positive rate with respect to the false positive rate is around 1 for the dataset considered in this study (Table 1).…”

Section: Discussion Of the Results Of Classifying Tumor Images Using ...mentioning

confidence: 97%

Developing a convolutional neural network for classifying tumor images using Inception v3

Mahmood

Sadeq

Hamodi

et al. 2023

EEJET

View full text Add to dashboard Cite

Deep learning algorithms rely on digital pathology to classify tissue tumors, where the whole tissue slides are digitized and imaged. The produced multi-resolution whole slide images (MWSIs) are with high resolution that may range from about 100,000 to 200,000 pixels. MWSIs are often stored in a multi-resolution configuration to simplify the processing of images, navigation, and efficient exposition. This work develops a network for classifying MWSIs that require high memory employing a deep neural Inception-v3 architecture. This work employs the MWSIs from Camelyon16, which is around 451 GB in size of Challenge dataset from two independent sources including 400 MWSIs as a total of lymph nodes. The training dataset contains 111 MWSIs of tumor tissue and lymph nodes and 159 WSIs of normal lymph nodes. The developed model uses sample-based processing to train extensive MWSIs employing the MATLAB platform. The model introduces transfer learning techniques with an Inception-v3-based architecture to categorize separate samples as a tumor or normal. Therefore, the main aim here is to achieve two-classes binary segmentation containing normal and tumor. This includes creating a new fully connected layer for the Inception-v3 architecture with two classes and compensating new layers instead of the original final fully-connected layers. The results obtained demonstrated that the heatmap visualization can recognize the boundary coordinates of ground truth as sketchy Region Of Interest (ROI), where the green boundary represents the normal regions and the tumor area with red boundaries. The proposed Inception v3 Convolutional Neural Network (CNN) architecture can achieve more than 92.8 % accuracy for such MWSIs dataset to categorize brain tumors into normal and tumor tissue

show abstract

Section: Discussion Of the Results Of Classifying Tumor Images Using ...mentioning

confidence: 97%

Developing a convolutional neural network for classifying tumor images using Inception v3

Mahmood

Sadeq

Hamodi

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

“…The Laplace transform and deep learning CNN solutions show excellent consistency for the steady solutions and there is a good agreement between the obtained transients. Future work can be recommended when comparing the solving of such symbolic differential equations of the 2-loop RLC AC circuit with deep learning convolutional neural network [33], [34].…”

Section: Laplace Transform Resultsmentioning

confidence: 99%

Modeling two loops RLC circuit AC power source using symbolic arithmetic differential equations

Hassan,

Abed,

H. Sabry

2024

Bulletin EEI

View full text Add to dashboard Cite

As oscillator applications, resistance-inductor-capacitor (RLC) circuits are employed in a diversity of settings. A low-pass, band-stop, band-pass, or high-pass filters can all be designed using an RLC circuit. A two-loop RLC circuit could not be represented mathematically in prior studies. Laplace transform is one type of integral transformation, which is able to resolve both second order non-uniform and uniform linear differential equations. This work solves the differential equations (DEs) of a two loops RLC circuit of an alternating voltage source by using two alternative approaches, Laplace transform (LT) and deep learning convolutional neural network (DLCNN). Initially, two DE have been declared. Next, Laplace transform is computed to solve these equations with symbolic variables for the first loop current and capacitor charge. Finally, we substitute the numerical values of the circuit elements for the symbolic variables. The charge and current initially decline exponentially. On the other hand, they oscillate over a long period of time. The capacitor charge and current initially decline exponentially and oscillate over a long period of time. The qualities of the result can be examined with a symbolic result, which is not possible with a numeric result.

show abstract

“…It is proposed to use the U-Net CNN [15,16] to segment images of objects in aerial photographs. To improve the efficiency of the neural network, this model was trained by a set of aerial photographs (Fig.…”

Section: Discussion Of Results Of Studying the Semantic Segmentation Of Images Of Objects In Aerial Photographs Using Cnnmentioning

confidence: 99%

“…Our review of the literature [15,16] showed that the U-Net model demonstrates high efficiency for the semantic segmentation of images of objects of different shapes and positions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 97%

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

View full text Add to dashboard Cite

This paper considers a model of the neural network for semantically segmenting the images of monitored objects on aerial photographs. Unmanned aerial vehicles monitor objects by analyzing (processing) aerial photographs and video streams. The results of aerial photography are processed by the operator in a manual mode; however, there are objective difficulties associated with the operator's handling a large number of aerial photographs, which is why it is advisable to automate this process. Analysis of the models showed that to perform the task of semantic segmentation of images of monitored objects on aerial photographs, the U-Net model (Germany), which is a convolutional neural network, is most suitable as a basic model. This model has been improved by using a wavelet layer and the optimal values of the model training parameters: speed (step) ‒ 0.001, the number of epochs ‒ 60, the optimization algorithm ‒ Adam. The training was conducted by a set of segmented images acquired from aerial photographs (with a resolution of 6,000×4,000 pixels) by the Image Labeler software in the mathematical programming environment MATLAB R2020b (USA). As a result, a new model for semantically segmenting the images of monitored objects on aerial photographs with the proposed name U-NetWavelet was built. The effectiveness of the improved model was investigated using an example of processing 80 aerial photographs. The accuracy, sensitivity, and segmentation error were selected as the main indicators of the model's efficiency. The use of a modified wavelet layer has made it possible to adapt the size of an aerial photograph to the parameters of the input layer of the neural network, to improve the efficiency of image segmentation in aerial photographs; the application of a convolutional neural network has allowed this process to be automatic.

show abstract

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Cited by 12 publications

References 18 publications

Developing a convolutional neural network for classifying tumor images using Inception v3

Developing a convolutional neural network for classifying tumor images using Inception v3

Modeling two loops RLC circuit AC power source using symbolic arithmetic differential equations

Improving a neural network model for semantic segmentation of images of monitored objects in aerial photographs

Contact Info

Product

Resources

About