Tongue shape classification integrating image preprocessing and Convolution Neural Network

2017 ◽  
Author(s):  
Chun-Mei Huo ◽  
Hong Zheng ◽  
Hong-Yi Su ◽  
Zhao-Liang Sun ◽  
Yi-Jin Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Shape Classification ◽  
Image Preprocessing

scholarly journals GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 124989-124998 ◽  
Author(s):  
Dilong Li ◽  
Xin Shen ◽  
Yongtao Yu ◽  
Haiyan Guan ◽  
Hanyun Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Convolution Neural Network ◽  
Shape Classification ◽  
Geometric Moments

scholarly journals Detection of Lung Cancer from MRI Scan Images Using Image Processing Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 1860-1866
Author(s):  
Pushpalatha S. Nikkam
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Image Processing ◽  
Early Detection ◽  
Computer Aided Design ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Image Preprocessing ◽  
Binary Images ◽  
Magnetic Resonance Imaging Mri

Lung cancer is one of the causes of death. Early detection of lung cancer can save the life of a patient. The detection is done by many different techniques i.e, image processing, Computer-Aided Design, etc. Digital image processing is the latest emerging tool in the medical field where researchers used for the early detection of cancer. Magnetic resonance imaging(MRI stands) of the lungs of the patient from the lung image database is used as input data for image preprocessing. In preprocessing stage conversion of the RGB image to the grayscale image takes place. Grayscale images are further converted to binary images. After image-processing, the input images become more efficient and refined. These input images are used for the convolution neural network. Convolution filtering, Max pooling filtering steps are been in CNN which will train the dataset to predict whether lung image is cancerous (Malignant) or Non-cancerous(Benign).In this paper image processing procedures such as image preprocessing, segmentation feature extraction have been implemented for different algorithms such as Support Vector Machine, K- Nearest Neighbour, Convolution Neural Network, Artificial Neural Network using the image and the CSV files with the result of 100%, 66%, 97.70%, and 83% respectively.

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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