scholarly journals Extracting Novel Features for Skin Burn Image Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 1890-1896
Keyword(s):  
Texture Features ◽  
Image Features ◽  
Burn Injuries ◽  
Automated Classification ◽  
Skin Burn ◽  
Burn Treatment ◽  
Depth Classification ◽  
Linear Svm ◽  
Burn Depth

In this paper, the objective is to propose a set of novel features for the classification of different burn depths by using an image mining approach. Both colour and texture features were studied on skin burn dataset comprising skin burn images categorized into three burn depths by the burn specialist. The performance of the proposed feature set was evaluated using linear SVM on 10-fold cross validation method. The empirical results showed that the six proposed novel features, when used together with the common image features, was the best set of features that was able to classify most of the burn depths in terms of accuracy, precision and recall measures with the values of 96.8750%, 96.9697% and 96.6667% respectively. Automated classification of skin burn depths is essential because the initial burn treatment provided to patients are usually based on the first evaluation of the skin burn injuries by determining the burn depths. However, the burn specialist may not always be available at the accident site. In conclusion, the features extracted that represent the burn characteristics specifically in terms of colour and texture were able to effectively characterise the depth of burns in accordance to burn depth classification.

Comparison of BI-RADS Lesion Descriptors and Computer-Extracted Image Features for Automated Classification of Malignant and Benign Breast Lesions

2003 ◽  
pp. 317-321 ◽  
Author(s):  
Yulei Jiang ◽  
Robert M. Nishikawa ◽  
Robert A. Schmidt ◽  
Carl J. D’Orsi ◽  
Carl J. Vyborny ◽  
...  
Keyword(s):  
Image Features ◽  
Breast Lesions ◽  
Automated Classification ◽  
Benign Breast Lesions

Finite Element-Based Analysis of Bio-Heat Transfer in Human Skin Burns and Afterwards

2020 ◽  
pp. 2041010
Author(s):  
Abul Mukid Md. Mukaddes ◽  
Ryuji Shioya ◽  
Masao Ogino ◽  
Dipon Roy ◽  
Rezwan Jaher
Keyword(s):  
Heat Transfer ◽  
Finite Element ◽  
Human Skin ◽  
Burn Injuries ◽  
Heat Transfer Analysis ◽  
Skin Damage ◽  
Skin Burn ◽  
Burn Treatment ◽  
Thermal Burn ◽  
Finite Element Software

This research was conducted to develop the three-dimensional (3D) finite element model of human skin for bio-heat transfer analysis. The skin burn was analyzed using Penne’s bio-heat equation, which has been adopted in many commercial finite element software. Burn injuries mostly occur due to heat transfer from hot object, hot liquids, cooking flames, and sometimes due to exposure to chemicals, electricity, and ionizing radiation. Depending upon the condition and duration of exposing, thermal burn may cause severe skin damage. The burn effect on human skin under the contact with a hot object or hot fluid was analyzed in this paper. The burn intensity in terms of degrees of burn was measured with different burning conditions and their corresponding time was graphically shown. Using the temperature profile obtained from the analysis, various methods of burn treatment were evaluated and compared. The results from this analysis will help to understand human skin burn under different burning conditions and treatment of different burn injuries.

Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D

2008 ◽  
Vol 18 (12) ◽  
pp. 2745-2755 ◽  
Author(s):  
H. F. Boehm ◽  
C. Fink ◽  
U. Attenberger ◽  
C. Becker ◽  
J. Behr ◽  
...  
Keyword(s):  
Texture Features ◽  
Automated Classification ◽  
Pulmonary Tissue

scholarly journals Big transfer learning for automated skin cancer classification

2021 ◽  
Vol 23 (3) ◽  
pp. 1611
Author(s):  
Zinah Mohsin Arkah ◽  
Dalya S. Al-Dulaimi ◽  
Ahlam R. Khekan
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Image Features ◽  
Cancer Classification ◽  
Automated Classification ◽  
Novel Approach ◽  
Learned Features ◽  
Manual Classification

<p>Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.</p>

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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