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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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Related Experiment Video

Updated: Jul 2, 2025

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Published on: November 25, 2011

Developing an efficient method for melanoma detection using CNN techniques.

Devika Moturi¹, Ravi Kishan Surapaneni², Venkata Sai Geethika Avanigadda²

¹Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India. devikamoturi@gmail.com.

Journal of the Egyptian National Cancer Institute

|February 26, 2024

Summary

This study highlights the effectiveness of deep learning for skin cancer detection. A customized Convolutional Neural Network (CNN) achieved 95% accuracy, outperforming MobileNetV2, and is now available via a web application.

Keywords:

Customized CNN Deep learning Flask HAM10000 Melanoma MobileNetV2 Skin cancer detection

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Last Updated: Jul 2, 2025

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

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

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

Published on: August 18, 2022

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Skin cancer is a prevalent and potentially fatal disease with rising global incidence.
Melanoma, a primary subtype, is clinically aggressive and responsible for most skin cancer deaths.
Early detection through screening is crucial for effective skin cancer management.

Purpose of the Study:

To evaluate deep learning techniques for accurate and rapid skin cancer detection.
To compare the performance of MobileNetV2 and a customized Convolutional Neural Network (CNN) for classifying malignant and benign skin tumors.
To develop a user-friendly web application for skin lesion image analysis.

Main Methods:

Utilized the HAM10000 dataset, comprising 10,000 skin lesion images.
Applied deep learning models, specifically MobileNetV2 and a customized CNN, for tumor classification.
Compared the diagnostic performance of the implemented deep learning techniques.

Main Results:

The customized CNN model achieved a diagnostic accuracy of 95%, surpassing MobileNetV2's 85%.
A web application was developed using a Python framework, featuring a graphical user interface (GUI).
The GUI enables users to input patient details, upload lesion images, and receive predictions on malignancy and affected percentage.

Conclusions:

Customized CNN demonstrated superior accuracy in melanoma detection compared to MobileNetV2.
The developed web application provides an accessible tool for preliminary skin cancer assessment.