DermaSense.ai
Bringing AI to the forefront of dermatology.
Created on 23rd February 2025
•
DermaSense.ai
Bringing AI to the forefront of dermatology.
The problem DermaSense.ai solves
Challenges of Dermatological Diagnosis:
- Clinical Efficiency Barriers : Traditional dermatological diagnosis requires extensive examination time, leading to longer wait times and reduced patient throughput.
- Healthcare Access Limitations : Many regions face severe shortages of qualified dermatologists, creating significant barriers to timely care.
- Diagnostic Accuracy Challenges : Many skin conditions share similar visual characteristics, increasing the risk of misdiagnosis without advanced diagnostic tools.
- Healthcare Disparity Issues : Limited access to specialized dermatological care creates significant healthcare inequities, particularly in rural and underserved areas.
DermaSense.ai addresses the need for early and accurate detection of skin diseases, making it easier and safer for individuals to monitor their skin health. Here’s how it solves critical problems:
Early Detection: Detects skin conditions like melanoma, eczema, and psoriasis early, promoting timely treatment.
Convenience: Allows users to analyze their skin from home by simply uploading images, avoiding the need for in-person visits.
Monitoring: Helps users track skin changes over time for proactive care.
Accessibility: Makes dermatological services available to anyone, anywhere, bridging the gap in underserved areas.
Cost-Effective: Offers an affordable alternative to costly dermatologist visits.
Awareness: Increases awareness of skin conditions and when to seek professional help.
Challenges we ran into
One big challenge we faced while building DermaSense.ai was training the AI model. The problem was with the Google Colab time limit. Here's what happened:
The Issue:
• Training the model took a lot of time because of the complex data and deep learning techniques used.
• Google Colab only lets you run a session for a certain amount of time. When the time limit was reached, the training would stop, and all the progress would be lost. This meant I had to start over every time, which was really frustrating.
Solution:
To solve this, I used model checkpoints. This means I saved the model’s progress at regular points during training.
• If the session was interrupted, I could load the saved progress and continue without starting from scratch.
• I also set it up so the best version of the model (the one with the highest accuracy) would be automatically saved.
Outcome:
• By using checkpoints, I didn’t lose my progress and could continue training the model without starting over each time.
• This made the whole training process smoother and more efficient, allowing me to get the best results without worrying about interruptions.
Tracks Applied (1)