(AI) for automatic diagnosis based on MRI/CT scans

AI & Machine Learning, Python — incorrectly specified categories?

3441 USD

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The system must self-learn when connected to databases of medical institutions, analyzing images and textual diagnoses for accurate diagnosis and disease prediction. Key functions of the MVP

Medical image processing: • Uploading and preprocessing MRI and CT images in formats (DICOM and others). • Automatic analysis of images and highlighting pathologies.
Text data analysis: • Extracting information from textual descriptions of diagnoses. • Matching textual information with images for training and improving diagnostics.
Self-learning and adaptation: • Continuous self-learning of AI based on new data from connected databases. • Analyzing feedback from doctors and adapting models based on this information.
User interface: • A pop-up window with analysis results when opening an image. • A field for comments from the doctor with the possibility of voice input. Technical requirements 1. Image processing model: • Use of pre-trained models for analyzing medical images, such as U-Net and ResNet. • Integration with PyTorch and TensorFlow libraries. 2. Text processing model: • Use of NLP models for analyzing textual diagnoses, such as BERT and BioBERT. • Integration with Hugging Face Transformers libraries. 3. Infrastructure and integration: • Cloud data storage and processing on servers (e.g., AWS, Google Cloud). • Integration with APIs of medical institutions' databases for automatic data collection. 4. User interface: • Development of a web interface based on modern technologies (React, Angular). • Implementation of features for interaction with doctors (pop-ups, comments). Implementation steps 1. Data collection: • Set up API access to medical institutions' databases. • Collect and anonymize data for training models. 2. Model development: • Configure and train pre-trained models for image analysis (U-Net, ResNet). • Configure and train pre-trained models for text analysis (BERT, BioBERT). • Combine the results of image and text analysis to improve diagnostic accuracy. 3. Integration and testing: • Integrate models into a single product. • Conduct testing on real data and improve algorithms based on results. 4. Interface development: • Create a prototype of the user interface. • Integrate the interface with the main AI functions (pop-ups, comments). 5. Self-learning and adaptation: • Set up self-learning mechanisms based on new data. • Implement a feedback analysis system from doctors to improve models.

Proposals 10

Mykhailo Pavlov

60 0

Budget: 4000 EUR Deadline: 30 days

Good day!

We are ready to implement a system that will self-learn when connected to databases of medical institutions, analyzing images and textual diagnoses for accurate diagnosis and disease prediction.

We write in Python. We rank 5th on the platform for this language.

Portfolio: Freelancehunt

We would be happy to collaborate!

Dmitry Golub

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Projects -
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Rating 141

Budget: 3000 EUR Deadline: 30 days

day3000 EUR

Greetings! We are an outsourcing company providing services of professional developers and designers. Thanks to our cooperation, orders are completed as quickly and efficiently as possible. We have a large portfolio with many different projects developed using a wide range of technologies: Python, JavaScript

Tair W

93 1

Projects 96
Rating -
Rating 4 264

Budget: 3000 EUR Deadline: 20 days

https://freelancehunt.com/project/pyopengl/1208018.html

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