duygut/detection_of_cancer_cells_over_blood_microscopy_images-

Detecting white blood cancer cells based on the collected images by pattern recognition and machine learning techniques.

Detection of cancer cells over blood microscopy images based on shape anomaly

The codes in this repository have been implemented in MATLAB.

• The aim of this project is detecting white blood cancer cells based on the collected images by pattern recognition and machine learning techniques.

• The project has 6 main topics:

1. Image acquisition
2. Image preprocessing
3. Image Segmentation
4. Morphologic Operations
5. Feature Extraction
6. Clasification

• In this project the different results have been shown with different methods and their impact on classification accuracy.
  • CIELAB Lab color segmentation and K-Means clustering used for image segmentation,
  • Watershed algorithm applied to detect and separate overlapped white blood cells.
  • Texture, statistical and geometrical features are used for feature extraction of white blood cells
  • Support Vector Machine and Multilayer Perceptron Neural Network used for classification.

In total 108 images were analyzed and up to 95% accuracy has been achieved.

Dataset can be request in below link

https://homes.di.unimi.it/scotti/all/