MATLAB code of leukemia Detection || Blood cancer detection

Hello, friends today I am posting one project in the field of biomedical imaging. Now a days biomedical field gains much importance. The title of the project is leukemia Detection or Blood cancer detection using Digital Image Processing. 

The implementation of the project in matlab is shown below in the following YouTube video. Just have a look.  

The block diagram of the project is shown below. The detection uses the following digital image processing concepts.
-- Image Acquisition
-- Segmentation
-- Morphological processing
-- classify using various classifiers. 

Block Diagram of blood cancer detection

There are multiple steps involved in using segmentation and morphology analysis to detect blood cancer. This is a basic overview of how you could go about completing this task:

1. Data Acquisition: Use a microscope to take pictures of blood samples. Different formats, such as brightfield or fluorescent microscopy, are possible for these images.

2. Preprocessing: To improve the quality and prepare the images for segmentation, preprocess the images. This could involve normalization, contrast enhancement, and denoising.

3. Segmentation: It's important to separate the white blood cells (WBCs) from the surrounding tissue. A variety of segmentation techniques are available, including edge detection and thresholding, as well as more sophisticated approaches like watershed segmentation and deep learning-based segmentation.

4. Morphological Analysis: After the cells have been segmented, you can use morphological analysis to identify characteristics that point to cancerous cells. These characteristics could include the ratio of the nucleus to the cytoplasm, texture, size, and irregularities in shape.

5. Feature Extraction: Take pertinent information out of the divided cells. Calculating fundamental morphological characteristics like area, perimeter, circularity, solidity, eccentricity, etc., may be necessary for this.

6. Classification: Determine whether a cell is malignant or normal by using the features that were extracted. For classification, you can use a variety of machine learning or deep learning algorithms, including convolutional neural networks (CNN), random forests, and support vector machines (SVM).

7. Validation: Use relevant metrics, such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves, to validate the performance of your classification model.

8. Iterative Improvement: Utilizing the performance metrics that were acquired during validation, improve your segmentation and classification algorithms. This could entail adjusting settings, experimenting with different algorithms, or gathering additional training data.

9. Clinical Validation: After your model exhibits encouraging outcomes, use clinical data to further validate it to make sure it works as intended in practical situations.

10. Implementation: Integrate your trained model into a diagnostic system to help pathologists detect blood cancer more precisely and quickly.

MATLAB Implementation of the project: 

Blank GUI

MATLAB code of leukemia detection or blood cancer detection

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