Skinner: supporting Scalp Health Management with eXplainable AI

Hair-related illnesses may significantly impact the Quality of Life of people suffering from them. Deep Learning models and techniques may be adopted for early detection of these diseases. In this paper, we propose a mobile app, named Skinner, aims to support the end-users in the detection of their skin disease. The app’s aim is only to alert the user, it does not substitute the clinicians, indeed it suggests the most appropriate medical center in the neighborhood. Skinner is based on a Resnet50 neural network that reaches 78.84on ten classes of disease. It also adopts explainable AI techniques for improving transparency and the user’s trust.

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Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1109/bibm62325.2024.10822794
Citations
1
Primary Topic
Digital Imaging in Medicine
Type
article
Field-Weighted Citation Impact
0.54
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article

Skinner: supporting Scalp Health Management with eXplainable AI

Rita Francese, Laura De Santis, Gabriele Lodato
1 citations
Digital Imaging in Medicine
0.54
article

Skinner: supporting Scalp Health Management with eXplainable AI

Rita Francese, Laura De Santis, Gabriele Lodato
article en
1 citations

Abstract

Hair-related illnesses may significantly impact the Quality of Life of people suffering from them. Deep Learning models and techniques may be adopted for early detection of these diseases. In this paper, we propose a mobile app, named Skinner, aims to support the end-users in the detection of their skin disease. The app’s aim is only to alert the user, it does not substitute the clinicians, indeed it suggests the most appropriate medical center in the neighborhood. Skinner is based on a Resnet50 neural network that reaches 78.84on ten classes of disease. It also adopts explainable AI techniques for improving transparency and the user’s trust.

University of Salerno (IT)
Zero hunger
Openalex Percentile: Top 25%
Digital Imaging in Medicine
0.54
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