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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Detecting melanoma in dermoscopy images using scale adaptive local binary patterns.

Farhan Riaz, Ali Hassan, Muhammad Younis Javed

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 9, 2015

Summary

This summary is machine-generated.

This study introduces a new method combining texture and color features for melanoma detection in dermoscopy images, achieving strong classification performance. The approach utilizes advanced local binary patterns and HSV histograms for improved diagnostic accuracy.

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Area of Science:

Medical image analysis
Computer vision applications in dermatology

Background:

Melanoma detection relies on visual characteristics like texture and color.
Computer vision aids in developing assisted diagnostic systems for melanoma.

Purpose of the Study:

To propose a combined texture and color feature approach for melanoma classification in dermoscopy images.
To evaluate the effectiveness of the proposed feature set against existing methods.

Main Methods:

Utilizing a variation of Local Binary Patterns (LBP) for scale-adaptive texture feature extraction.
Employing standard HSV histograms for color feature extraction.
Concatenating texture and color features for Support Vector Machine (SVM) classification.

Main Results:

The proposed combined feature set demonstrated good classification performance.
The method favorably compared to other state-of-the-art alternatives in experiments.

Conclusions:

Combining texture and color features offers a promising approach for melanoma classification.
The developed method shows potential for enhancing assisted diagnostic systems in dermatology.