AI-Assisted Skin Cancer Detection

Enhancing skin cancer by diagnosing user’s skin images on their mobile phones, improving dermatological care's accuracy, accessibility, and efficiency.

Healthcare

2020

Client and Industry

This US-based healthcare start-up specializes in early-stage skin cancer detection solutions.

Industry

Healthcare

Business Type

Start-up

Services

Development & Deployment

Our Role

We developed an AI-powered dermatology diagnostic tool to enhance diagnostic accuracy and accessibility. This involved creating machine learning models for classification and segmentation and developing a mobile app that connects users with healthcare professionals.

Project Goals

01

Accurate Diagnosis: Utilize inputs to classify skin lesions as benign or malignant and identify specific lesion types, thereby enhancing diagnostic precision.

02

Instant Accessibility: Enable users to capture skin images with their mobile phones and receive immediate diagnoses within the app.

03

Comprehensive Development: Develop robust machine learning models for classification and segmentation, along with a user-friendly mobile app to facilitate these capabilities.

Business
Challenges

01.

Data Quality Assurance: Ensure the high quality of images and their labels, which must be verified by medical professionals to maintain diagnostic accuracy.

02.

Realistic Data Collection: Obtain and validate data captured using phone cameras to accurately reflect real-life scenarios, crucial for the app’s effectiveness on mobile devices.

Solution

We harness machine learning to boost the precision, efficiency, and accessibility of dermatological care

Key Features

01

Benign/Malignant Classification: Our advanced ML model proficiently classifies skin lesions as benign or malignant, a critical step in determining the urgency and approach of treatment.

02

This innovative approach helps dermatologists by offering preliminary analyses for quicker review and confirmation, enhancing diagnostic accuracy, reducing workload, and speeding up treatment.

03

Lesion Type Detection and Segmentation: The model excels in identifying specific types of lesions and accurately segments them, providing vital details about their size, shape, and boundaries, which are essential for treatment planning and monitoring.