SkinSpectra: Revolutionizing Dermatological Diagnosis with AI Integration

Dermatological diagnosis is crucial for identifying and treating a wide range of skin conditions, but traditional methods often suffer from subjectivity, variability, and the need for specialized expertise. SkinSpectra, leveraging artificial intelligence (AI) technology and advanced image analysis techniques, emerges as a solution to these challenges, promising rapid, accurate, and accessible diagnosis.

Developed upon Convolutional Neural Networks (CNNs), SkinSpectra is trained on the HAM10000 dataset, comprising thousands of high-quality images of various skin lesions. This extensive dataset provides a rich source of information for the AI model to learn and recognize patterns indicative of different skin conditions, enabling accurate diagnostic predictions.

Integration with Gemini API via the Flutter platform enhances SkinSpectra's functionality and usability. Gemini API grants access to a plethora of medical resources and facilitates seamless communication between the AI model and the user interface. This integration ensures efficient data exchange and real-time interaction with the user, enhancing the overall user experience.

Key Features and Benefits of SkinSpectra:

• Accurate Diagnosis
• User-Friendly Interface
• Real-Time Results
• Accessibility
• Cost-Effectiveness

SkinSpectra represents a significant advancement in dermatological diagnosis, offering a novel approach that combines AI technology, comprehensive datasets, and user-friendly interfaces. By leveraging CNN algorithms and integrating with Gemini API via Flutter, SkinSpectra redefines how skin conditions are diagnosed and managed, ultimately improving patient care and outcomes. With its potential to enhance accessibility, accuracy, and efficiency in dermatological care, SkinSpectra holds promise for transforming the landscape of dermatology and empowering healthcare professionals worldwide.

Challenges we ran into

• Dataset Quality: Ensuring the quality and diversity of the dataset (HAM10000) posed challenges in obtaining representative images for various skin conditions.

• Model Training: Training the Convolutional Neural Network (CNN) requires significant computational resources and expertise to optimize performance and accuracy.

• Integration Complexity: Integrating SkinSpectra with Gemini API via Flutter required careful coordination and troubleshooting to ensure seamless communication and functionality.

• User Interface Design: Designing a user-friendly interface that meets the needs of healthcare professionals while maintaining simplicity and efficiency presented challenges in balancing functionality and aesthetics.

• Privacy and Security: Ensuring compliance with data privacy regulations and implementing robust security measures to protect patient information posed challenges in maintaining confidentiality and data integrity.

• Validation and Testing: Validating SkinSpectra's efficacy and reliability through rigorous testing and validation processes required extensive collaboration with healthcare professionals and validation studies.

User Interface

App Demo

skinspectra.mp4

💻Installation

• Clone the Repository and Change the directory.

  flutter pub get
  flutter run

🧑🏻‍💻Run

Clone the repository and change directory.

  git clone https://github.com/Atharva-Werulkar/skinspectra.git

Go to the project directory

  cd skinspectra

Flutter pub get and run

  flutter pub get
  flutter run

📄License

Distributed under the MIT License. See License for more information.

🖊️Authors

• Atharva Werulkar - ([email protected])
• Mrunal Thakre - ([email protected])
• Nidhi Manyam - ([email protected])
• Amol Jaiswal - ([email protected])

Contributing

Contributions are always welcome! Your feedback will help us grow as a developer and build better and more reusable apps.

Please adhere to this project's code of conduct.