The document outlines the development of a convolutional neural network (CNN) model for malaria detection using blood smear images, addressing the high incidence of malaria. It discusses the model's architecture, performance, and potential deployment as a mobile app, achieving an accuracy of about 98%. Recommendations for improvement include better data labeling and additional dataset collection for validation.

1. by Emmanuel Baisire
Malaria Detection Model Using Convolutional Neural Network

2. Agenda
Discussion Topics
• Global Malaria Health Risks
• Data Preprocessing and Exploration
• Solution Design
• Recommended Model Architecture
• Opportunities and Challenges

3. 3
Deep Learning Model
Problem Statement:
• According to WHO, Malaria infected 241 millions cases and 627,000 deaths in 2021.
• It is transmitted through mosquito bites leading to high fever, chills and even death.
• Diagnostic tools include microscopes by experienced nurses, rapid diagnostic test and polymerase chain
reaction.
• Current methods are time consuming, requires a trained microscopist to read hundreds of smear images to
detect malaria.
• Build a deep learning model that can automatically detect malaria parasite in blood smear images.

4. 4
Deep Learning Model
Data Exploration
• Malaria cell images dataset contained 24,958 train and 2,600 test images.
• Test and Train images were of pixel values ranging from 0 to 255.
• Parasite and uninfected labels were balanced for Train and Test data.
• Parasitized blood cells have visibly red marks on any side of the image while uninfected images have no
marks.

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Deep Learning Model
Solution Design
• A Convolution Neural Network architecture was preferred due to its strengthen to classify images.
• The problem was a binary classification to determine if an image had a parasite or uninfected
• A total of 6 models were trained and tested to choose the best performing models
• They were divided into 4 distinct CNNs and 2 VGG type neural networks

6. 6
Deep Learning Model
Solution Design
• Base Model with 3 layer of 32 filter size • Best Performing Model 3 layers with BatchNormLzn

7. 7
Deep Learning Model
Recommended Model Architecture
• Best Performing Model with 3 Conv2D layers with 32 filters
• LeakyReLu Activation Function
• Dense Layer with 512 filters 3 layers
• MaxPoolling2D of 2 by 2 windows and BatchNormalization
• Last Binary Classification layer using Softmax activation function

8. 8
Deep Learning Model
Recommended Model Performance

9. 9
Deep Learning Model
Recommended Model Performance

10. 10
Deep Learning Model
Model Deployment
• It is a great candidate as a Phone App to provide diagnostic results
• user will scan cell images to the mobile app to get patient’s positive or negative results.
• The model will be deployed using TensorFlow REST API with less little infrastructure and network
maintenance costs.
• CNN deep learning-based model performed better than other experimental models.
• It was able to identify malaria parasites at an accuracy rate of about 98% with relatively few cell
images.

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Deep Learning Model
Recommendations and Conclusions
• Perform further review of the cell images that seemed to be wrongly labeled during data collection.
• Collect more cell image dataset sample with varying parasite markers.
• Seek expert knowledge from skilled microscopist to review mislabeled images by the model.
• Image dataset should be split into 3 sets that include training, validation and test datasets. Instead of
using only 2 datasets.
• For validation purpose, I would also recommend the K-Fold validation use by splitting the data into 3
partitions
• In conclusion, the general architecture for this solution involved data-preprocessing to ensure that we
are able feed the input data into a model to predict Parasitic and uninfected people at 98% accuracy.