scholarly journals MRI Brain Abnormality Detection Using Conventional Neural Network (CNN)

2021 ◽  
Author(s):  
Jeevitha R ◽  
Selvaraj D
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Tumours ◽  
Input Image ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Brain Abnormality ◽  
Mri Brain ◽  
Skull Stripping ◽  
Fuzzy C Means Algorithm

Brain tumours has huge heterogeneity and there is always a familiarity between normal and abnormal tissues and hence the extraction of tumour portions from normal images becomes persistent. In this paper, MRI brain tumor detection is performed from a brain images using Fuzzy C-means(FCM) algorithm and sebsequently Convolutional Neural Network(CNN) algorithm is employed. Here, firstly preprocessing step is performed by Skull Stripping algorithm followed by Segmentation process. Fuzzy C-means algorithm is used to segment the Cerebrospinal Fluid(CSF), Grey matter(GM) and White Matter(WM) from the database. The third part is to extract features to find whether the tumor is present or not, here eleven features are extracted like mean, entropy, S.D(Standard Deviation). The final part is the classification process done by Convolutional Neural Network(CNN) in which it is able to differentiate whether the input image is normal image or an abnormal image. Compared to other methods, here the values of the features extracted are higher for normal images than for abnormal Images and it is shown from the graphs drawn from the extracted features.

scholarly journals Incorporating FCM and Back Propagation Neural Network for Image Segmentation

2014 ◽  
pp. 16-21
Author(s):  
Ebrahim. Aghajari ◽  
Dr.Mrs. Gharpure Damayanti
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Input Image ◽  
Discrete Wavelet ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
The Membership Function

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

NeuroImage ◽  
2016 ◽  
Vol 129 ◽  
pp. 460-469 ◽  
Author(s):  
Jens Kleesiek ◽  
Gregor Urban ◽  
Alexander Hubert ◽  
Daniel Schwarz ◽  
Klaus Maier-Hein ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Mri Brain ◽  
Brain Extraction ◽  
Skull Stripping

Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

2020 ◽  
Vol 65 (6) ◽  
pp. 759-773
Author(s):  
Segu Praveena ◽  
Sohan Pal Singh
Keyword(s):  
Neural Network ◽  
Acute Lymphoblastic Leukaemia ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lymphoblastic Leukaemia ◽  
Input Image ◽  
Deep Convolutional Neural Network ◽  
Grey Wolf ◽  
Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

scholarly journals Research on the Extraction of Image Edge Information in Convolutional Neural Networks

2021 ◽  
Vol 2083 (3) ◽  
pp. 032015
Author(s):  
Guanru Zou ◽  
Yulin Luo ◽  
Zefeng Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Image ◽  
Edge Weight ◽  
Edge Information ◽  
Image Dimension ◽  
Original Dataset ◽  
Image Edge ◽  
Accuracy Rates ◽  
Object Move

Abstract Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.

Sign in / Sign up

Export Citation Format

Share Document