Multi-type skin diseases classification using OP-DNN based feature extraction approach

Feature Extraction of Melanoma Data using Machine Learning Techniques

Design Engineering ◽

10.17762/de.vi.3967 ◽

2021 ◽

pp. 5352-5360

Author(s):

R.Veeralakshmi, Dr.K.Merriliance

Keyword(s):

Feature Extraction ◽

Image Segmentation ◽

Skin Diseases ◽

Region Of Interest ◽

Texture Features ◽

Extraction Methods ◽

Input Image ◽

The Body ◽

Machine Learning Techniques ◽

Segmentation Technique

In our body the skin is the largest organ, it protects from injury, infection and also helps to maintain the temperature of the body. Melanoma Skin cancer is one of the most dangerous skin diseases and it is caused by an uncontrolled growth of abnormal skin cells, by ultraviolet radiation from sunshine. Melanoma is more common among white skins such as Americans than in darker skins. The digital lesion images have been analyzed based on image acquisition, pre-processing, and image segmentation technique. The image segmentation technique is applied to easily identify the affected portion in the skin input image. The images are enhanced using morphological filters and sharpen region of interest in an image, enhancement method enhanced the non-uniform background illumination and converts the input image into a binary image too easy to identify foreground objects. The mole of melanoma is segmented from the background using Active Contour algorithm. After that, the feature extraction methods such as Kernel PCA, SIFT are used to extract melanoma affected area in an image based on their intensity and texture features.

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A Study on Deep Learning Methods for Skin Disease Classification

International Journal of Engineering and Management Research ◽

10.31033/ijemr.11.2.7 ◽

2021 ◽

Vol 11 (2) ◽

pp. 48-52

Author(s):

N.Vanitha ◽

M.Geetha

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Skin Diseases ◽

Disease Classification ◽

Learning Technology ◽

The People ◽

Learning Techniques ◽

Initial Stage ◽

Human Labor ◽

Tone Color

Dermatological disorders are one among the foremost widespread diseases within the world. Despite being common its diagnosis is extremely difficult due to its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision-based techniques (deep learning) to automatically predict the varied sorts of skin diseases. The system makes use of deep learning technology to coach itself with the varied skin images. the most objective of this technique is to realize maximum accuracy of disease of the skin prediction. The people health quite the other diseases. Skin diseases are mostly caused by mycosis, bacteria, allergy, or viruses, etc. The lasers advancement and Photonics based medical technology is employed in diagnosis of the skin diseases quickly and accurately. The medical equipment for such diagnosis is restricted and costliest. So, Deep learning techniques helps in detection of disease of the skin at an initial stage. The feature extraction plays a key role in classification of skin diseases. The usage of Deep Learning algorithms has reduced the necessity for human labor, like manual feature extraction and data reconstruction for classification purpose.

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Content Based Image Retrieval Using Lacunarity and Color Moments Of Skin Diseases

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v9.i1.pp243-248 ◽

2018 ◽

Vol 9 (1) ◽

pp. 243

Author(s):

I Gusti Ayu Triwayuni ◽

I Ketut Gede Darma Putra ◽

I Putu Agus Eka Pratama

Keyword(s):

Feature Extraction ◽

Skin Diseases ◽

Content Based Image Retrieval ◽

Texture Characteristic ◽

Color Feature ◽

Characteristic Extraction ◽

Moments Method ◽

Extraction Test ◽

Combined Test ◽

Normalization Process

<p><em>The research conducted contributed in the form of CBIR application which was developed using texture and color feature extraction in searching the contents information of an object of skin disease image. The textured feature is extracted using Lacunarity, while for color feature extraction using Color Moments as well as a combination of both methods. The results of color characteristic extraction test using Color Moments Method yielded images corresponding to 100% similarity percentages and experimentation of texture characteristic extraction using Lacunarity Method yielded images corresponding to a percentage of suitability of 25%, followed by a combined test of both methods and the normalization process produces images corresponding to a percentage of conformity of 60%.</em></p>

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Applying Texture Feature Extraction for Skin Diseases using GLCM and GLRLM

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2019.5023 ◽

2019 ◽

Vol 7 (5) ◽

pp. 147-153

Author(s):

Prof. Mrs. K. Chandra Prabha

Keyword(s):

Feature Extraction ◽

Skin Diseases ◽

Texture Feature ◽

Texture Feature Extraction

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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