scholarly journals Medical images Compression using convolutional neural network with LWT

2018 ◽  
Vol 6 (6) ◽  
Author(s):  
Surbhit Shukla ◽  
Anugrah Srivastava
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Images

scholarly journals DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Teng ◽  
Hang Li ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Related Area ◽  
Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

Improving resolution of medical images with deep dense convolutional neural network

2018 ◽  
Vol 32 (1) ◽  
Author(s):  
Shuaifang Wei ◽  
Wei Wu ◽  
Gwanggil Jeon ◽  
Awais Ahmad ◽  
Xiaomin Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Images

A Convolutional Neural Network-Based Framework for Medical Images Analyzing in Enhancing Medical Diagnosis

2020 ◽  
Vol 23 (13) ◽  
Author(s):  
Ayad Hameed Mousa ◽  
Zahraa Noor Aldeen ◽  
Ali Hussein Mohammed ◽  
Mohammed G. K. Abboosh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Diagnosis ◽  
Medical Images

scholarly journals Survey on CNN based super resolution methods

2021 ◽  
Vol 2 (4) ◽  
pp. 27-33
Author(s):  
Rafaa Amen Kazem ◽  
Jamila H. Suad ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Medical Images ◽  
Super Resolution ◽  
Low Resolution ◽  
Resolution Mapping ◽  
Super Resolution Mapping ◽  
Output Stages

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

scholarly journals REPLICATION IMAGE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Author(s):  
K. Kalaiselvi ◽  
◽  
S. Saranya ◽  
K. Deepa Thilak ◽  
K. Kumaresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Conventional Method ◽  
Medical Images ◽  
Image Data ◽  
Image Detection ◽  
Convolution Model ◽  
Ease Of Access

The ease of access to image data has led to overuse of repeated images at various instances which leads to increases duplication and redundancy in many industries. Advanced editing techniques which are available very easily, encourages original copyrights images to be misused. This results in lack of originality in data generated at every level. Common solutions include allowing manual selections of duplicate images or compares images pixel by pixel. The conventional method is to use 3 branch Siamese Convolution model to detect duplication of medical images. We propose to develop a model to detect duplication in everyday images by training a Siamese Convolutional Neural Network and try to achieve greater accuracy than previously developed solutions. Using Grad-Cam network inspection we propose to inspect the decisions taken by the CNN upon detecting duplication in images.

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