Disease detection using blood cell analysis is a common diagnostic technique where the characteristics of blood cells such as size, shape and number are analyzed to determine any abnormalities. Several diseases can be detected including anemia, leukemia and infectious diseases like malaria and HIV. In Python, machine learning techniques can be used for disease detection using blood cells which involves collecting and preprocessing blood cell images, selecting and training a model, extracting features, evaluating the model, and making predictions.

1. DISEASE DETECTION
USING BLOOD CELLS
A.BENJEO
R.GOULNATH
A.EBISANKAR
S.LOGESHWARAPANDI
J.MICHEAL JEROME

2. Disease detection using blood cells is a common diagnostic
method used in modern medicine.
The basic principle behind this technique is to analyze the
characteristics of blood cells,
such as their size, shape, and number, to determine if there
are any abnormalities present in the body.

3. There are several diseases that can be detected using blood
cell analysis, including anemia, leukemia, and infectious
diseases such as malaria and HIV.
In the case of anemia, for example, a low red blood cell count
or abnormal morphology of red blood cells can indicate the
presence of the disease.

4. Detecting diseases using blood cells in Python can be done
through various machine learning techniques.
Here's a general outline of the steps you can take to perform
disease detection using blood cells in Python:

5. You need to gather data from blood cell images for
training and testing purposes. You can use publicly
available datasets or create your dataset by collecting
images of blood cells.
DATA
COLLECTION
Preprocessing is an essential step before training the
model. You can perform image processing techniques
such as normalization, grayscale conversion, contrast
enhancement, and filtering to enhance the quality of
the images.
PREPROCESSIN
G There are several machine learning models to choose
from, such as Random Forest, K-Nearest Neighbors,
Support Vector Machines (SVM), and Convolutional
Neural Networks (CNN). The choice of model
depends on the complexity of the problem and the
size of the dataset.
MODEL
SELECTION
Feature extraction is the process of extracting
relevant features from the blood cell images. You can
use various feature extraction techniques such as
Histogram of Oriented Gradients (HOG), Local Binary
Patterns (LBP), and Convolutional Neural Networks
(CNN) to extract features.
FEATURE
EXTRACTION
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6. After selecting the model, you need to train it on the
extracted features. You can use the Scikit-learn or
Keras library in Python to train the model.
MODEL
TRAINING
Once the model is trained, you need to evaluate its
performance on the test data. You can use metrics
such as accuracy, precision, recall, and F1 score to
evaluate the performance of the model..
MODEL
EVALUATION
Display the disease information.
DISPLAY
After the model is trained and evaluated, you can use
it to predict the disease from the blood cell images.
PREDICTION
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7. “
”
Blood cells are the silent story tellers of our
health, and through their detection, we can
unravel the mysteries of disease and pave the
way for early invention and prevention.

8. THANK YOU