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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM42750

Deep Learning and Blockchain for Accurate Skin Cancer and Disease Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM42750

Volume:

9 |

3 |

Mar 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM42750

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DS

Dr.A. Suneetha1

BN

B. Niharika2

AM

A. Meghana3

BK

B.V. Kousalya4

BP

B.Bhavyasri Pravallika5

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Abstract

Skin cancer is a significant global health concern, primarily caused by excessive exposure to ultraviolet (UV) radiation. If not detected and treated early, skin cancer can spread to vital organs such as the lungs, brain, and liver, complicating treatment and reducing survival rates. Early detection is crucial for maximizing recovery chances and improving patient outcomes. This research proposes a deep learning-based application to enhance the accuracy and efficiency of skin cancer and disease prediction. This study combines Artificial Intelligence (AI) and blockchain technology to create a secure and efficient system. Patients can upload skin images, which are analysed by AI models, such as convolutional neural networks (CNNs) and logistic regression, to predict skin diseases, including cancer. Blockchain technology ensures data immutability, transparency, and security. Based on AI predictions, the system recommends verified doctors based on specialty and availability. This innovative approach aims to provide a reliable solution for early detection and treatment of skin cancer and related diseases.

KEYWORDS

Convolutional neural networks DenseNet ResNet EfficientNet blockchain smart contracts.

Cite This Article

Dr.A. Suneetha1, B. Niharika2, A. Meghana3, B.V. Kousalya4, B.Bhavyasri Pravallika5, "Deep Learning and Blockchain for Accurate Skin Cancer and Disease Detection," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, Mar 2025. https://doi.org/10.55041/IJSREM42750

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Publication Date

3/26/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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