Malaria Classification using CNN algorithm-Deep Learning techniques

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Malaria Classification using CNN algorithm-Deep Learning techniques with multiple class classification is achieved with good accuracy.

What are the algorithm Used in the project?

Malaria classification using CNN algorithmis done using two algorithms, one is machine learning and other is deep learning

  1. Decision Tree
  2. Convolution Neural Network (CNN)

What is the project Objective?

The objective of our study is to implement malaria cell type detection system using Convolutional Neural Networks (CNN) from images. There are four classes of detection can be considered, Falciparum, Malariae, Ovale and Vivax detection. Our objective is to implement a machine learning algorithm and a deep learning algorithm and compare their performance. The main aim to classify the given input image to one of four classes by the trained model from input dataset.

What is the number of class or disease types identified?

Falciparum, Malariae, Ovale and Vivax are the types of Malaria disease identified using this project. This is a multi-class classification model. The experimental result shows that our CNN model achieved around 70% of accuracy.

Malaria Classification using CNN algorithm-Deep Learning techniques

How to check the demo of the project?

Please visit under here

DO YOU WANT TO CHECK THE PROJECT ABSTRACT? Call 9600095046

Similar type of project can checked in our following links

Breast Cancer Detection using CNN algorithm

Liver Disease detection using CNN algorithm

https://www.finalsemprojects.com/liver-disease-prediction-through-machine-learning-and-deep-learning/

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