DIAGNOSTICA

Inspiration

The development of AI-powered medical image analysis platforms aligns with the goal of achieving sustainable healthcare. By utilizing AI to analyze medical images, healthcare providers can improve the accuracy of diagnoses, resulting in better treatment plans and outcomes for patients. This can reduce the need for unnecessary tests and procedures, which can decrease healthcare costs and promote more efficient use of resources.

Additionally, AI-powered medical image analysis can aid in medical research by providing a vast amount of data that can be analyzed to identify new patterns and insights. This can lead to the development of new treatments and therapies, further advancing the field of healthcare.

Overall, the integration of AI-powered medical image analysis platforms into healthcare systems can contribute to more sustainable and effective healthcare practices.

What it does

AI-powered medical image analysis platform help doctors and medical staff make faster, more accurate diagnoses at lower costs.

Identifying cancers in its early stages, which contributes to a high cure rate .
Detect multiple diseases such as tuberculosis, breast cancer, Covid-19, and more

Implementation process

Implementing a mammogram AI analysis tool using Streamlit and a Convolutional Neural Network (CNN) on the MIAS dataset involves several steps. Here's a general overview of the implementation process:

Prepare the MIAS dataset: The MIAS dataset consists of digitized mammograms that have been annotated by expert radiologists.
- Download the MIAS dataset and store it in a folder.
- Write a script to clean, preprocess, and normalize the dataset. This include tasks such as resizing the images, adjusting the contrast, and cropping the images.
- Split the dataset into training, validation, and testing sets.
Train the CNN model:
- Define the CNN model architecture.
- Compile the model using a suitable loss function and optimizer.
- Train the model on the training set using Keras or TensorFlow. Monitor the training progress using metrics such as accuracy and loss.
- Validate the model on the validation set to prevent overfitting.
Build the Streamlit web app:
- Install Streamlit and other required packages.
- Write a script to build the web app interface. The interface include an upload section for the mammogram image and a button to initiate the analysis.
- Preprocess the uploaded image before running it through the CNN model.
- Use the CNN model to analyze the mammogram image and display the analysis results to the user.
Deploy the web app

Accomplishments that we're proud of

DIAGNOSTICA won the Sustainable Leaders Bootcamp in the thematic of Health, organized by Leancubator in the scope of the regional action #Rethink_Med launched in the Med Dialogue for Rights and Equality with the support of the European Union dedicated to student project holders in the field of health, food, and the environment. This experience has given me a deep understanding of the challenges that startups face, and the importance of identifying and nurturing promising ideas. I was also honored to be selected as a semi-finalist in the Entrepreneurship World Cup Algeria selection, and I have been offered a spot in the Microsoft startup program