ABSTRACT.ppt
- 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 01 02 03 04
- 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 05 06 07 08
- 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