YOLOv8 Medical Imaging

YOLO is known for its ability to detect objects in an image in a single pass, making it a highly efficient and accurate object detection algorithm.🎯

The latest version of YOLO, YOLOv8, released in January 2023 by Ultralytics, has introduced several modifications that have further improved its performance.

In this project, I will focus on three major computer vision tasks that YOLOv8 can be used for: classification, detection, and segmentation. I will explore how YOLOv8 can be applied in the field of medical imaging to detect and classify various anomalies and diseases🧪💊.

Introduction to YOLOv8

Some of the notable modifications in YOLOv8 include:

• New Backbone Network: YOLOv8 adopts the powerful Darknet-53 as its backbone network, enhancing feature extraction capabilities.

• Anchor-Free Detection: YOLOv8 employs an anchor-free detection head, which directly predicts the center of an object instead of relying on offset values from predefined anchor boxes.

• New Loss Function

Tasks

In this project, I focus on three major computer vision tasks using YOLOv8, all accessible through the Streamlit web application:

1. Classification: Utilize the YOLOv8 model to classify medical images into three categories: COVID-19, Viral Pneumonia, and Normal, using the COVID-19 Image Dataset.

2. Object Detection: Employ YOLOv8 for detecting Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets in blood cell images using the RBC and WBC Blood Cells Detection Dataset.

3. Segmentation: Use YOLOv8 for segmenting breast ultrasound images with the Breast Ultrasound Images Dataset.

Screenshots

I used Streamlit to create a user-friendly interface for easy interaction with the YOLOv8 model. Below are screenshots of each part:

About page

Object Detection

Classification

Segmentation

Installation and Usage

Installation

1. Clone this repository to your local machine:

   git clone https://github.com/sevdaimany/YOLOv8-Medical-Imaging.git

2. Navigate to the project directory:

   cd YOLOv8-Medical-Imaging

3. Create a virtual environment (optional but recommended):

   python -m venv venv

4. Activate the virtual environment:

On Windows:

venv\Scripts\activate

On macOS and Linux:

source venv/bin/activate

5. Install the required dependencies from the provided requirements.txt file:

   pip install -r requirements.txt

Usage

Using the Provided Demo Images

I've made it easy for you to get started with our project without the need to download a dataset. I've included a set of demo images in the DEMO_IMAGES directory. You can use these images to quickly see how our project works.

Run the Streamlit App:

Start the Streamlit app to see our project in action:

streamlit run app.py