scholarly journals Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches

2017 ◽  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Data

A JAVA environment for medical image data analysis: Initial application for brain PET quantitation

1998 ◽  
Vol 23 (3) ◽  
pp. 207-214 ◽  
Author(s):  
K. Mikolajczyk ◽  
M. Szabatin ◽  
P. Rudnicki ◽  
M. Grodzki ◽  
C. Burger
Keyword(s):  
Data Analysis ◽  
Medical Image ◽  
Image Data ◽  
Initial Application ◽  
Brain Pet ◽  
Pet Quantitation ◽  
Medical Image Data

Strong fuzzy c-means in medical image data analysis

2012 ◽  
Vol 85 (11) ◽  
pp. 2425-2438 ◽  
Author(s):  
S.R. Kannan ◽  
S. Ramathilagam ◽  
R. Devi ◽  
E. Hines
Keyword(s):  
Data Analysis ◽  
Medical Image ◽  
Image Data ◽  
Fuzzy C Means ◽  
Medical Image Data

scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2020 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Image ◽  
Image Data ◽  
Random Regression ◽  
Feature Engineering ◽  
Anatomical Structures ◽  
Bounding Box ◽  
Medical Image Data ◽  
2D And 3D

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.<br>

scholarly journals Medical Image Data Classification Using Deep Learning Based Hybrid Model with CNN and Encoder

2020 ◽  
Vol 34 (5) ◽  
pp. 645-652
Author(s):  
Bhanu Prakash Battula ◽  
Duraisamy Balaganesh
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Image Data ◽  
Data Classification ◽  
Healthcare Sector ◽  
Model Classification ◽  
Proposed Model ◽  
Medical Image Data ◽  
Image Data Classification

Healthcare sector is one of the prime and different from other trade. Society expects high priority and highest level of services and care irrespective of money. Presently medical field suffers from accurate diagnosis of diseases and it create huge loss to society. The prime factor for this is due to the nature of medical data, it is a combination of all varieties of data. Medical image analysis is a key method of Computer-Aided Diagnosis (CAD) frameworks. Customary strategies depend predominantly on the shape, shading, and additionally surface highlights just as their mixes, a large portion of which are issue explicit and have demonstrated to be integral in medical images, which prompts a framework that does not have the capacity to make portrayals of significant level issue area ideas and that has poor model speculation capacity. In this paper we are attempting a medical image data classification technique using hybrid deep learning technique based on Convolutional Neural Network (CNN) and encodes. What's more, we assess the proposed approach on two benchmark clinical picture datasets: HIS2828 and ISIC2017. The proposed algorithm is applied on the considered 2 datasets for performing data classification using deep learning based CNN and encoders. The proposed model is compared with the traditional methods and the results show that proposed model classification accuracy is better than the existing models.

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