scholarly journals Framework for comprehensive enhancement of brain tumor images with single-window operation

2020 ◽  
Vol 10 (1) ◽  
pp. 801
Author(s):  
Deepthi Murthy T. S. ◽  
Sadashivappa G.
Keyword(s):  
Brain Tumor ◽  
Image Enhancement ◽  
Medical Image ◽  
Significant Contribution ◽  
Linear Filter ◽  
Radiological Images ◽  
Multi Level ◽  
Image Enhancing ◽  
Non Linear Filter

Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.

A case study on medical image enhancement based on II curvelet

2015 ◽  
Vol 26 (3-4) ◽  
pp. 129-138
Author(s):  
Guo Qi ◽  
Shi Fei ◽  
Shen Shu-ting
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Image Enhancement

scholarly journals The Problem of Success and Failure in Public-private Innovation Partnerships

2021 ◽  
pp. 1-21
Author(s):  
JONATHAN HAMMOND ◽  
SIMON BAILEY ◽  
OZ GORE ◽  
KATH CHECKLAND ◽  
SARAH DARLEY ◽  
...  
Keyword(s):  
Public Sector ◽  
Health Service ◽  
Policy Maker ◽  
Policy And Practice ◽  
Policy Success ◽  
Political Effects ◽  
Innovation Project ◽  
Multi Level ◽  
Test Beds

Abstract Public-Private Innovation Partnerships (PPIPs) are increasingly used as a tool for addressing ‘wicked’ public sector challenges. ‘Innovation’ is, however, frequently treated as a ‘magic’ concept: used unreflexively, taken to be axiomatically ‘good’, and left undefined within policy programmes. Using McConnell’s framework of policy success and failure and a case study of a multi-level PPIP in the English health service (NHS Test Beds), this paper critically explores the implications of the mobilisation of innovation in PPIP policy and practice. We highlight how the interplay between levels (macro/micro and policy maker/recipient) can shape both emerging policies and their prospects for success or failure. The paper contributes to an understanding of PPIP success and failure by extending McConnell’s framework to explore inter-level effects between policy and innovation project, and demonstrating how the success of PPIP policy cannot be understood without recognising the particular political effects of ‘innovation’ on formulation and implementation.

scholarly journals 3-D Block-Rooting Scheme With Application to Medical Image Enhancement

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 3880-3893
Author(s):  
Viacheslav Voronin ◽  
Aleksander Zelensky ◽  
Sos Agaian
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Image Enhancement

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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