Performance evaluation of medical image processing algorithms

2000 ◽  
Author(s):  
James C. Gee
Keyword(s):  
Image Processing ◽  
Performance Evaluation ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

scholarly journals AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Diagnostics ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 217
Author(s):  
Liyang Wang ◽  
Angxuan Chen ◽  
Yan Zhang ◽  
Xiaoya Wang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Actinic Keratosis ◽  
Medical Image ◽  
Histopathological Examination ◽  
Diagnostic System ◽  
Medical Image Processing ◽  
Deep Cnn ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

scholarly journals Editorial

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Uranchimeg Tudevdagva
Keyword(s):  
Image Processing ◽  
Embedded Systems ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Self Organizing

This editorial introduces the issue of Embedded Self-organizing Systems (ESS) journal. Main focus of this issue is application of image processing algorithms, medical image processing scenarios and usage of embedded systems in different areas.

Some Implementation of MATITK on Medical Image Processing

2013 ◽  
Vol 718-720 ◽  
pp. 2035-2039
Author(s):  
Yu Wen Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Filtering ◽  
Medical Image Segmentation ◽  
Simple Function ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Medical image processing includes many basic components such as medical image filtering, medical image segmentation and medical image registration, whose advanced algorithms can be found in ITK platform. But the ITK is difficult for the beginners. Only simple function is used to call dozens of image processing algorithms by MATITK . Therefore by using MATITK , the students can master these advanced algorithms and the improved implementation results can be obtained.

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