Smartphone-based AI-assisted diagnosis of pigmented skin lesions using dermoscopic images.

Project Abstract

Smartphone-based AI-assisted diagnosis of pigmented skin lesions using dermoscopic images leverages mobile technology and deep learning to provide accurate, accessible, and rapid preliminary assessment of skin cancers at the point of care. This study develops and validates a robust mobile pipeline that collects dermoscopic images via a smartphone adapter, preprocesses data to standardize illumination and scale, and applies a multimodal AI framework that combines lesion-level features with contextual metadata to differentiate malignant melanoma, basal cell carcinoma, squamous cell carcinoma, and benign pigmented lesions. The research integrates a convolutional neural network backbone with attention mechanisms to highlight diagnostically relevant regions, complemented by a secondary classifier that incorporates patient age, sex, lesion location, lesion diameter, and a history of sun exposure. To address data heterogeneity and scarcity, the project compiles a diverse, multi-center dermoscopic image corpus augmented with synthetic minority oversampling and transfer learning from large dermatology datasets, while adhering to ethical standards and patient privacy. The model's performance is quantified using rigorous metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), precision-recall curves, and balanced accuracy across subtypes, with particular emphasis on early-stage melanoma detection. A cross-validation protocol and external validation on an independent dataset assess generalizability to real-world smartphone captures under varying lighting, focus, and device specifications. The study also investigates model explainability through saliency maps and Grad-CAM visualizations to provide clinicians with interpretable rationale for AI-driven predictions, fostering trust and clinical auditability. In addition, the research explores deployment feasibility in low-resource settings by evaluating on-device inference latency, energy consumption, and data privacy compliance, as well as user-interface design for non-specialist users. Comparative analyses against expert dermoscopy assessments and dermatologist-led teledermatology triage assess concordance and potential reductions in referral burden. The project further examines the impact of pre-processing techniques—color normalization, mossy-contrast enhancement, and artifact removal (e.g., hair, bubbles)—on classification performance, and conducts ablation studies to identify critical components of the pipeline. Potential pitfalls, including dataset bias, overfitting, and misclassification risk of rare lesion types, are systematically addressed through techniques such as stratified sampling, calibration of probabilistic outputs, and continuous learning mechanisms. The deliverables include a deployable smartphone application prototype with an edge-optimized AI model, a comprehensive benchmark dataset with standardized annotations, and a reproducible evaluation framework for future dermatology AI research. By delivering an accessible, scalable diagnostic aid, the project aims to augment early cancer detection, support triage decisions in primary care, and democratize dermatological expertise without compromising patient safety or data integrity.

Project Overview

What This Project Is About

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A straightforward look at using a smartphone to help identify pigmented skin lesions by analyzing dermoscopic images. The project investigates how a simple app can support early skin cancer screening by flagging suspicious moles or lesions.

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The Problem It Addresses

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Many people lack easy access to dermatology clinics, leading to delays in detecting dangerous skin conditions. Dermoscopic analysis by specialists is accurate but not always available. This project explores a mobile-friendly approach to assist non-specialists in recognizing warning signs.

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Objectives of the Project

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1. Understand the basics of skin imaging and dermoscopy.
2. Develop a simple smartphone workflow to capture and preprocess lesion images.
3. Train a basic AI model to classify lesions as benign or suspicious.
4. Evaluate the model’s accuracy using a public dataset and user testing.
5. Assess usability and potential real-world impact of the app.

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What You Will Do Step by Step

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1. Learn essential concepts in photography for clear lesion images and basic image preprocessing. 2. Gather and prepare a dataset of dermoscopic images. 3. Build a lightweight AI model suitable for phones. 4. Test the model on held-out images and compare with existing methods. 5. Create a user-friendly app prototype and perform simple user testing. 6. Analyze results and discuss limitations and improvements.

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Expected Outcome

An accessible mobile tool that can assist in initial lesion screening, with a clear report on its accuracy, limitations, and potential for integration into routine skin-health checks.