- Project Overview
- Avyuct Intellidetect
- Collaborative Enhancement
- Contributors
- Ownership and Copyright
- Repository Structure
- Documentation
- License
- Acknowledgements
Welcome to VisIQ, the collaborative space dedicated to advancing the capabilities of Avyuct's innovative product, Intellidetect. Intellidetect represents a transformative approach to healthcare diagnostics, harnessing the power of artificial intelligence within a cloud-based Software as a Service (SAAS) platform. Focused on early disease detection, Intellidetect streamlines diagnostic processes through a three-step workflow: dataset creation, model building with customizable deep learning algorithms (including ResNet18, VGG-16, AlexNet, GoogLeNet, MnasNet, and ResNet-50), and anomaly detection and segmentation. This repository serves as a hub for contributors passionate about enhancing the accuracy and efficiency of Intellidetect, even in the absence of its source code. The project's mission is to push the boundaries of AI-driven healthcare innovation, offering a flexible, adaptable, and powerful platform that contributes to significant advancements in diagnostic precision. Join us in shaping the future of healthcare diagnostics and explore the repository for documentation, resources, and guidelines on contributing to this impactful project. Together, we aim to make a positive impact on healthcare diagnostics and further propel Avyuct Intellidetect to new heights.
Avyuct Intellidetect stands at the forefront of healthcare diagnostics, leveraging artificial intelligence within a Software as a Service (SAAS) platform. The application streamlines the diagnostic process through a three-step workflow: dataset creation, model building with leading-edge deep learning algorithms, and anomaly detection and segmentation. With a focus on early disease detection, Intellidetect offers flexibility, adaptability, and powerful AI capabilities to healthcare professionals.
The product utilizes industry-wide used open-source neural network algorithms for classification and segmentation tasks. The following algorithms are employed:
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Classification Algorithms:
- ResNet18
- VGG-16
- AlexNet
- GoogLeNet
- MnasNet
- ResNet-50
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Segmentation Algorithm:
- U-Net
This repository serves as a collaborative hub for contributors passionate about advancing healthcare diagnostics. While the source code for Intellidetect may not be available, there are numerous ways to actively contribute to its improvement. Whether through data insights, model configurations, or other non-source code contributions, your input is valuable in enhancing the overall accuracy and efficiency of Avyuct Intellidetect.
We extend our gratitude to the following individuals who have contributed to the VisIQ project:
Thank you for being a part of the VisIQ community and for your dedication to advancing healthcare innovation!
Avyuct Intellidetect is a product of Avyuct, and all rights and ownership belong to the company. The use and distribution of this repository are subject to the terms and conditions set by Avyuct.
Explore the repository to find resources and documentation that guide contributors in enhancing the accuracy of Avyuct Intellidetect:
- README.md: Introduction and overview of the VisIQ project.
- docs: Detailed documentation for the enhancement project.
- tests: Test cases and testing-related resources.
- LICENSE: MIT License file.
For insights into the enhancement project's goals, guidelines, and usage, explore the documentation. Together, we aim to push the boundaries of AI-driven healthcare innovation.
This project is licensed under the MIT License. Your commitment to collaboration aligns with the open-source spirit of this project.
The VisIQ team acknowledges and appreciates the contributions of all contributors to this enhancement project. Your dedication drives the continuous improvement of Avyuct Intellidetect, shaping a future where healthcare diagnostics reach new heights.