What question did this study set out to answer?

To develop and evaluate a full-stack, deep learning-based automated prediction system for detecting and classifying multiple skin diseases from clinical images.

August 19, 2026International Journal of Technology & Emerging ResearchOpen Access

Skin Disease Prediction Software

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To develop and evaluate a full-stack, deep learning-based automated prediction system for detecting and classifying multiple skin diseases from clinical images.
Trained a convolutional neural network (CNN) model on a large dataset of skin disease images (HAM10000).
Constructed an end-to-end full-stack software architecture integrating a frontend interface, backend processing server, and database for patient record storage and prediction retrieval.
Demonstrated high accuracy and reliability in classifying multiple dermatological conditions from image inputs.
Achieved fast image processing and scalable record management suitable for clinical decision support.

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Implication

Machine learning study demonstrates high diagnostic accuracy and fast processing for automated skin disease detection, suggesting improved clinical accessibility in underserved areas.

Cite This Study

Tripathi et al. (2026) studied this question.

synapsesocial.com/papers/6a85632803308d306e2d6299 https://doi.org/10.64823/ijter.2604036

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Implication

Machine learning study demonstrates high diagnostic accuracy and fast processing for automated skin disease detection, suggesting improved clinical accessibility in underserved areas.

Skin Disease Prediction Software

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Implication

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Skin Disease Prediction Software

Key Points

Discussion

Implication

Cite This Study

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