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GitHub
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Dataset can be downloaded from Malarial Cell Images Dataset.
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Dataset consisits of 13780 parasited and 13780 non-infected images. A total of 27560 images are available in the dataset.
Some examples of the dataset are shown below.
| Image class | Image 1 | Image 2 | Image 3 | Image 4 | Image 5 |
|---|---|---|---|---|---|
| Parasite Image |  |
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| Non Infected Image |  |
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- Purple regions can be easily observed in the parasite images which are not available in Non-Infected images.
Now the task is to classify the given input image into Malaria or Normal class.
| Steps | Description | Result |
|---|---|---|
| 1 | Convert RGB to Gray Image |  |
| 2 | bw = Edge detection using sobel filter |  |
| 3 | bw005 = Edge detection using sobel filter with thresold = 0.05 |  |
| 4 | Subtract bw from bw005 |  |
| 4 | Dilate the bw005-bw image with structuring element of disk with radius=5 |  |
| 5 | If the area of the dilated image is more than the threshold then, image is classified as malarial infected sample. Thresold = 100 | Area = 937, So classified as Malaria  . |
Below are results are obtained by performing classification on 13780 Malaria adn 13780 Normal class images.
| Metric | Accuracy | Sensitivity | Specificity | Precision | Recall | F1-Score |
|---|---|---|---|---|---|---|
| Metric Value | 91.97 | 0.929 | 0.910 | 0.912 | 0.929 | 0.920 |
The proposed method solves the malaria image classification problem with good accuracy. A lot of other advanced methods like use of Classifiers on top extracted feature using feature extraction and more advanced deep learning algorithms like Deep CNNs. But there is a trade-off between computaional effiecieny, time required to train & inferece. The proposed method solves the problem with least possible resources and time constraints with comparable accuracy to more sophisticated methods.