Malaria is a deadly, infectious mosquito-borne disease caused by Plasmodium parasites. These parasites are transmitted by the bites of infected female Anopheles mosquitoes. While we won’t get into details about the disease, there are five main types of malaria. This project detects and classifies malaria using deep learning. With the regular manual diagnosis of blood smears, it is an intensive manual process requiring proper expertise in classifying and counting the parasitized and uninfected cells. Typically this may not scale well and might cause problems if we do not have the right expertise in specific regions around the world. Some advancements have been made in leveraging state-of-the-art (SOTA) image processing and analysis techniques to extract hand-engineered features and build machine learning-based classification models. However, these models are not scalable with more data being available for training and given the fact that hand-engineered features take a lot of time. Deep Learning models, or to be more specific, Convolution Neural Networks (CNNs) have proven to be really effective in a wide variety of computer vision tasks.
- Principal Component Analysis
- SVM classifier
Drawbacks of Exisitng System
- High Computational load and poor discriminatory power.
- SVM is slow training for large feature sets.
- Less accuracy in classification
- DTCWT and GLCM Features
- NN Classifier
- K-means Clustering
- The segmentation algorithm Proves to be simple and effective
- Greyscale Co-occurrence matrix performed well in NN
- Better texture and edge representation
- Segmentation provides better clustering efficiency
- 4 GB of RAM
- 500 GB of Hard disk
- MATLAB 2014a
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