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Exploring the revolutionary impact of Convolutional Neural Networks (CNNs) in detecting critical diseases such as lung, breast, and skin cancer, pneumonia, and COVID-19 through a systematic review of deep learning algorithms in medical imaging.

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Multimodal Medical Image Analysis using Deep Learning: Advancements in Segmentation, Registration and Disease Classification

This project focuses on the cutting-edge advancements in the field of medical image analysis, particularly in classification, utilizing deep learning techniques. It explores multimodal approaches to improve accuracy and efficiency in analyzing medical images.

Paper Link

For detailed insights and methodologies, refer to our comprehensive paper. Click Here to access the paper

Installation

This project requires specific Python libraries. To set up your environment, follow these steps:

  1. Clone the Repository: Clone this repository to your local machine or download the source code.

  2. Install Dependencies:

    • Navigate to the Install_dependencies folder.
    • If you are using a Unix-like system (Linux/Mac), run the install_dependencies.sh script.
    • If you are on Windows, run the install_dependencies.bat script.

    These scripts will install all the necessary Python libraries for the project.

Note: This installation guide is temporary and subject to change. Ensure you have Python and pip installed on your system before running these scripts.

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Exploring the revolutionary impact of Convolutional Neural Networks (CNNs) in detecting critical diseases such as lung, breast, and skin cancer, pneumonia, and COVID-19 through a systematic review of deep learning algorithms in medical imaging.

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