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386848

Skin Cancer Detection Using Deep Learning

Article

Last updated: 27 Apr 2025

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Tags

Artificial Intelligence

Abstract

Skin cancer, potentially life-threatening, highlights the need for early detection. Recent advancements in deep learning and mobile technology offer solutions. Deep learning, including CNNs, excels in medical image analysis, while smartphones provide ubiquitous information access. This convergence revolutionizes healthcare, particularly in dermatology, with deep learning enabling precise skin lesion detection on mobile devices. In this paper, we explore the synergy of deep learning and mobile technology for skin cancer detection, introducing a specialized algorithm optimized for mobile use. Our goal is twofold: accurate diagnosis with advanced AI and global accessibility, ultimately saving lives through early intervention [1].
We meticulously preprocessed the HAM10000 dataset, featuring 10,015 high-res images categorized into seven pigmented lesion classes, ensuring data integrity. Our Mobile Net V2 model achieves 98.5% accuracy in skin lesion classification, highlighting its clinical potential. Further fine-tuning is needed to reduce false negatives, supported by statistical analysis confirming our deep learning superiority.
We developed a mobile app compatible with various devices, enabling clinicians to quickly identify potential skin cancer cases and refer them for evaluation and treatment. Our vision is to have a lasting impact on skin cancer prevention and early detection through collaborations with healthcare institutions and dermatology experts. This includes expanding the app's capabilities for teledermatology consultations, and expediting diagnoses and interventions while upholding ethical data handling, privacy, and user trust.
In summary, this paper highlights the potential of deep learning and mobile technology to revolutionize skin cancer detection, providing a practical tool for early diagnosis and improved global patient outcomes.

DOI

10.21608/asc.2024.245718.1017

Keywords

Skin Cancer Detection, Deep learning, HAM10000, preprocessing, Melanoma

Authors

First Name

shimaa

Last Name

Osman

MiddleName

-

Affiliation

Higher Institute of Computers and Information Technology, Computer Depart., El. Shorouk Academy, Cairo, Egypt

Email

dr.shimaa.osman@sha.edu.eg

City

-

Orcid

-

First Name

Hany

Last Name

Maher Sayed Lala

MiddleName

-

Affiliation

Department of Mathematics, Faculty of Science, Al-Azhar, Cairo, Egypt

Email

hmsayed@azhar.edu.eg

City

-

Orcid

-

First Name

Bassant

Last Name

Sayed

MiddleName

-

Affiliation

Higher Institute of Computers and Information Technology, Computer Depart., El. Shorouk Academy, Cairo, Egypt

Email

bassant.sayed@sha.edu.eg

City

-

Orcid

-

Volume

14

Article Issue

1

Related Issue

44439

Issue Date

2023-06-01

Receive Date

2023-11-04

Publish Date

2023-06-01

Print ISSN

1687-8515

Online ISSN

2682-3578

Article File

ASC3868481685566800.pdf

PDF . 524.7kB

Link

https://asc.journals.ekb.eg/article_386848.html

Detail API

http://journals.ekb.eg?_action=service&article_code=386848

Order

386,848

Type

Original Article

Type Code

1,549

Publication Type

Journal

Publication Title

Journal of the ACS Advances in Computer Science

Publication Link

https://asc.journals.ekb.eg/

MainTitle

Skin Cancer Detection Using Deep Learning

Details

Type

Article

Created At

27 Dec 2024