Image Classification Base on Sparse Representation with Basis Design

2013 ◽  
Vol 709 ◽  
pp. 515-518
Author(s):  
Hao Feng Huang ◽  
Shao Ying Chen
Keyword(s):  
Representation Theory ◽  
Sparse Representation ◽  
Image Classification ◽  
Design Method ◽  
Classification Model ◽  
Classification Method ◽  
Sparse Model

An image classification method based on sparse representation with basis design is proposed. We construct a classification model under the sparse representation theory. The sparse model can lead to be better performance under a suitable dictionary, so the basis design method can follow the same process as discussed in[6].The experiments show that the proposed method improves the result.

Image Classification Based on Sparse Representation and ROI

2014 ◽  
Vol 556-562 ◽  
pp. 4906-4910
Author(s):  
Hui Hui Zhao ◽  
Jun Ding Sun
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Extraction Methods ◽  
Saliency Map ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Classification Method ◽  
Saliency Maps ◽  
Roi Extraction

A new image classification method based on regions of interest (ROI) and sparse representation is introduced in the paper. Firstly, the saliency map of each image is extracted by different methods. Then, we choose sparse representation to represent and classify the saliency maps. Four different ROI extraction methods are chosen as examples to evaluate the performance of the proposed method. Experimental results show that it is more effective for image classification based on ROI.

AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

2020 ◽  
Author(s):  
Xiaoyu He ◽  
Juan Su ◽  
Guangyu Wang ◽  
Kang Zhang ◽  
Navarini Alexander ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Image Classification ◽  
Bullous Pemphigoid ◽  
Skin Diseases ◽  
Pemphigus Vulgaris ◽  
Classification Model ◽  
Clinical Image ◽  
Clinical Images ◽  
Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

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