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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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Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques.

B Gunasundari¹, R Thiagarajan¹

¹Department of Computer Science and Engineering, Prathyusha Engineering College, Chennai, Tamil Nadu, India.

Cancer Investigation

|May 20, 2024

Summary

This study introduces Isomap with a vision transformer for improved skin cancer classification from high-dimensional images. This method enhances diagnostic accuracy for skin lesions, aiding in early detection and treatment.

Keywords:

Skin malignancy classification deep learning high-dimensional images performance evaluation skin lesion

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Skin cancer diagnosis relies on visual screening and pathological analysis.
High-dimensional image data presents challenges in accurate classification.
Deep learning methods are crucial for improving skin cancer detection rates.

Purpose of the Study:

To evaluate the effectiveness of Isomap combined with a vision transformer for skin cancer classification.
To improve the accuracy and efficiency of analyzing high-dimensional skin lesion datasets.
To enhance the classification of malignant versus benign skin lesions.

Main Methods:

Utilized Isomap, a nonlinear dimensionality reduction technique, to preserve intrinsic data structures.
Employed a vision transformer model for analyzing high-dimensional medical images.
Performed comparative analyses to evaluate classification performance metrics.

Main Results:

Isomap effectively reduced data complexity while retaining essential features for classification.
The combination of Isomap and vision transformer demonstrated improved accuracy in classifying skin lesions.
Nonlinear relationships in skin lesion data were better preserved, aiding differentiation.

Conclusions:

Isomap with a vision transformer offers a promising approach for accurate skin cancer classification.
This methodology enhances the analysis of complex, high-dimensional skin lesion datasets.
Improved classification accuracy can contribute to better patient outcomes in skin cancer detection.