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Malaria Disease Prediction System using Deep Learning with Flask App Project

246,19 kr 147,72 kr SEK

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The following contents are Downloadable immediately after the successful payment

  • Source Code

  • Flask App

  • Dataset

  • Trained M0del

  • Project Report (Both PDF and Word files)

  • PowerPoint Presentation (PPT)

  • Instructions to install the necessary Software and Libraries

  • Step-by-step instructions to execute the project

Project Description

Malaria is a serious illness. The root cause is insects that infect humans through the bite of female Anopheles mosquitoes. It can be cured if the right steps are taken. Microscopic diagnoses are poorly maintained and rely heavily on microscopist ability and knowledge. It is very common for microscopes to operate alone in low-cost settings, without a solid system in place that can guarantee the conservation of their capabilities and thus curable quality. This results in a wrong diagnostic conclusion in this area. Therefore, these facts have encouraged us to take up this building project. Diagnosis of Malaria through ML will benefit Health Care and assist in our studies as Machine Learning is a new benefit to the industry.
This final-year project is based on Malaria Disease Detection using Deep Learning. We have used MobileNet(150 CNN) and VGG16 to classify images. It was found that 150CNN provides better accuracy than VGG16 in our project. The model is first trained on the training set and then tested to classify the images as Parasitized or Uninfected.
In this project design and implementation of the deep neural networks, and learning are presented. We have used an approach and an algorithm to detect Malaria using Deep Learning. We have implemented an Artificial Neural Network and Convolution Neural Network used for the classification of the infected and uninfected images of blood samples.

Video Demonstration of Project

https://youtu.be/_2pubt2NKwg

 

Steps in Implementing Malaria Disease Prediction System using Deep Learning with Flask App Project:-

 The following steps are involved in the Methodology of our model -
  • Dataset Collection
  • Data Preprocessing
  • Data Augmentation
  • Proposing and Implementing Model

Dataset Description:

The dataset consisted of 27,560 cell images with the same number of parasitized and uninfected cell instances. 

  Training Testing
Parasitized 11024 2756
Uninfected 11024 2756
Total 22048 5512

Technical Specification

Language: Python

Libraries: Keras, TensorFlow, NumPy

Deep Learning Models Used

For classification: MobileNet(150CNN) and VGG16

 

Installation of required software and libraries

1. Extract the downloaded project folder.
2. Follow the video and Install the TensorFlow and CUDA toolkit
 

https://youtu.be/b9e3J-NJ8TY

Important Note:
If TensorFlow is not running properly, downgrade the Numpy version to 1.26.4 using the below command
>> pip install numpy==1.26.4
 
3. Open the Anaconda prompt and change the directory to the project folder
example:
cd path-of-project-folder
4. Switch to tf environment (which was created at the time of installing the TensorFlow) using the following command
>>> conda activate tf5. In tf environment, Install Requirements using the following command
>>> pip install -r requirements.txt
 

Follow the steps to train the model & run the App after installing the requirements.

1. Open the Anaconda prompt and change the directory to the project folder
example:
cd path-of-project-folder
 
2. Switch to tf environment using the following command
>>> conda activate tf
 
3. Open Jupyter Notebook using the following command
 
>> jupyter notebook
 
4. Once the Jupyter notebook is opened in the default browser, Open the Malaria_Detection.ipynb and run all the cells. 
 
5. Run the following command to launch Flask Webapp
>>> python app.py
 
6. The app is running at
 

Happy Learning

 Setup and modification are paid services based on requirements.