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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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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network.

Sumit Kumar Singh¹, Vahid Abolghasemi¹, Mohammad Hossein Anisi¹

¹School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

A novel computer-aided diagnosis system accurately classifies malignant skin lesions using advanced image processing and deep learning. This method improves early detection of skin cancer, aiding in timely treatment.

Keywords:

deep neural network image processing melanoma neutrosophic

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Area of Science:

Dermatology
Medical Imaging
Computer Science

Background:

Rising global incidence of skin cancer necessitates improved diagnostic tools.
Accurate and early detection of malignant lesions is crucial for patient survival rates.

Purpose of the Study:

To develop and validate a computer-aided diagnosis (CAD) system for classifying malignant skin lesions.
To enhance the accuracy and efficiency of skin cancer diagnosis through automated image analysis.

Main Methods:

Image pre-processing including artifact removal and histogram equalization.
Lesion segmentation using a Neutrosophic technique with a pentagonal structure.
Classification employing a deep neural network integrating Inception and residual blocks.

Main Results:

The segmentation model achieved high accuracy across multiple datasets (e.g., 99.50% on PH2).
The deep learning classifier demonstrated superior performance compared to existing methods.
Augmented datasets (103,554 images) improved classification accuracy.

Conclusions:

The proposed CAD system shows significant potential for accurate malignant skin lesion classification.
The integration of advanced image processing and deep learning offers a promising approach for skin cancer diagnosis.
This system can aid clinicians in early and precise identification of skin cancer.