Overview / Usage

The "MediScan" project is a cutting-edge solution designed to revolutionize the digitization of handwritten prescriptions within the medical industry. Leveraging the power of advanced artificial intelligence, the system employs a state-of-the-art Convolutional Neural Network (CNN) model, implemented using industry-standard tools such as TensorFlow and Keras. To further enhance performance and optimize computation, the project seamlessly integrates Intel extensions for TensorFlow, harnessing the capabilities of Intel hardware for accelerated training and inference. Additionally, the integration of Intel OneDAL contributes to efficient data preprocessing and manipulation, paving the way for streamlined image processing and text extraction from handwritten prescriptions. Through this innovative amalgamation of machine learning expertise and Intel's technology stack, the MediScan project aims to deliver unparalleled accuracy and speed in converting analog prescriptions into a structured digital format, ultimately enhancing patient care, reducing errors, and advancing the digitization of healthcare practices.

Methodology / Approach

The methodology employed by the "MediScan" project entails a systematic and robust approach to transforming handwritten prescriptions into digital form.

• Beginning with image capture, the system utilizes a user-friendly interface to obtain clear images of prescriptions. These images undergo preprocessing, wherein Intel OneDAL aids in optimizing image quality and format.
• The heart of the methodology lies in the implementation of a specialized Convolutional Neural Network (CNN) model, carefully crafted using TensorFlow and Keras.
• This CNN model, trained on diverse datasets of handwritten text, becomes adept at recognizing and extracting textual information from prescription images. The integration of Intel extensions for TensorFlow optimizes the CNN's performance, leveraging Intel's hardware capabilities for accelerated computation.
• Post-recognition, the extracted text is organized into structured digital records, enabling seamless integration with Electronic Health Record (EHR) systems.
• Rigorous validation and error-checking procedures, alongside collaboration with healthcare professionals, ensure the accuracy and reliability of the digitized prescriptions. Through this meticulously orchestrated methodology, the "MediScan" project contributes to the transformation of healthcare practices, bridging the gap between traditional prescriptions and a digitally empowered medical ecosystem.

Technologies Used

• At its core, the project leverages Convolutional Neural Networks (CNNs), a powerful subset of deep learning, implemented using TensorFlow and Keras frameworks.
• The inclusion of Intel extensions for TensorFlow enhances the CNN's computational efficiency, capitalizing on Intel's hardware capabilities for accelerated model training and inference.
• Intel OneDAL plays a pivotal role in the preprocessing stage, optimizing image quality and facilitating seamless data manipulation. This intelligent amalgamation of machine learning, deep neural networks, and Intel's technology stack empowers the "MediScan" system to meticulously extract and organize handwritten prescription text with exceptional precision.
• By melding these technologies, the project stands at the forefront of innovation, poised to redefine the way medical information is captured, processed, and integrated into the digital realm.

Repository

https://github.com/SDeBAS/MediScan/tree/main

Collaborators