An Evaluation of Convolutional Neural Nets for Medical Image Anatomy Classification

2016 ◽  
pp. 293-303 ◽  
Author(s):  
Sameer Ahmad Khan ◽  
Suet-Peng Yong
Keyword(s):  
Medical Image ◽  
Neural Nets

scholarly journals DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

2018 ◽  
Author(s):  
Wentao Zhu ◽  
Yeeleng S. Vang ◽  
Yufang Huang ◽  
Xiaohui Xie
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodule ◽  
Medical Image ◽  
Partial Information ◽  
Ct Images ◽  
Neural Nets ◽  
Training Dataset ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Weakly Supervised

AbstractRecently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms.1

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm

2018 ◽  
Vol 6 (1) ◽  
pp. 18-23 ◽  
Author(s):  
T.Gopi Krishna ◽  
◽  
K.V.N. Sunitha ◽  
S. Mishra ◽  
◽  
...  
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Medical Image
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