Enhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi

Nyatte, S. Samson; Perabi, Steve; Ondoa, G. Abessolo; Essiane, Salomé Ndjakomo; Ele, Pierre

doi:10.1007/978-981-99-0248-4_32

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Computational Cognitive Analysis of ADHD Patients using Matlab Applications

Kanna,

Mutheeswaran,

Jouda

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

View full text Add to dashboard Cite

Computational Cognitive Analysis of ADHD Patients using Matlab Applications

Kanna,

Mutheeswaran,

Jouda

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

View full text Add to dashboard Cite

Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices

Watt,

Chrysoulas,

Barclay

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Image classification usually requires connectivity and access to the cloud, which is often limited in many parts of the world, including hard-to-reach rural areas. Tiny machine learning (tinyML) aims to solve this problem by hosting artificial intelligence (AI) assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without Internet or cloud access. This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without Internet access. It was found that the developed prototype achieved a test accuracy of 78% when trained on the HAM10000 dataset, and a test accuracy of 85% when trained on the ISIC 2020 Challenge dataset.

show abstract

Enhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi

Cited by 2 publications

References 31 publications

Computational Cognitive Analysis of ADHD Patients using Matlab Applications

Computational Cognitive Analysis of ADHD Patients using Matlab Applications

Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices

Contact Info

Product

Resources

About