A Novel Approach for Identification of Brain Tumor by Combination of Intelligent Water Drop Algorithm and Convolutional Neural Network

2021 ◽  
pp. 237-245
Author(s):  
Ashish Kumar Dehariya ◽  
Pragya Shukla
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Water Drop ◽  
Novel Approach

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

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