Deep Learning for Skin Disease Diagnosis with End-to-End Data Security

Skin diseases, particularly skin cancer, pose significant health risks, necessitating accurate and timely diagnosis. Traditional manual analysis methods, however, are subject to errors and can be time-consuming. To address these challenges, we propose an automated classification system for skin dise...

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Bibliographic Details
Published in2023 Second International Conference on Informatics (ICI) pp. 1 - 6
Main Authors Pingulkar, Shriya, Divekar, Diti, Tiwary, Aryaman
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.11.2023
Subjects
CNN
data privacy and security
Data transfer
deep learning
Ethics
Firebase cloud database
image classification
Lesions
Medical diagnostic imaging
medical documentation
Medical services
Skin
Skin disease classification
skin lesions
SMTP
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Summary:Skin diseases, particularly skin cancer, pose significant health risks, necessitating accurate and timely diagnosis. Traditional manual analysis methods, however, are subject to errors and can be time-consuming. To address these challenges, we propose an automated classification system for skin disease detection that leverages deep learning algorithms to accurately diagnose skin lesions. This system facilitates secure sharing of diagnostic information among healthcare professionals, promoting collaborative treatment planning. Recognizing the critical importance of data security and confidentiality in the healthcare industry, our system employs state-of-the-art security measures to protect sensitive patient data whilst medical data transfer. By maintaining the integrity of patient information and adhering to regulatory requirements, our skin disease classification system provides healthcare professionals with fast and accurate diagnosis, and the confidence that patient privacy is upheld at all times.
DOI:10.1109/ICI60088.2023.10421188