To improve the accuracy and speed of malaria diagnosis, the project aims to distinguish Malaria infected human blood cells from the normal ones.

The repository contains the dataset of unknown variables and the script that is used to train this data

Detecting Malaria Parasites in Red Blood Cells using Machine Learning (Specifically CNNs)

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疟疾检测 Malaria detection

Malaria cells detection using CNN model.

Trained my first machine learning model using a public dataset of uninfected and parasitized cells images to detect malaria in humans with a low margin of error. Created a recursive model architecture with the following algorithm: image processing, grayscale conversion, contour detection, get areas of the 5 largest contours, and finally find the…

Code for Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

A convolutional neural network to classify blood cells between malaria infected and non-infected cases.

This project develops a machine learning-based onsite health diagnostic system, facilitating real-time analysis and early detection of health conditions. By integrating data from various sources, it offers personalized insights and enhances healthcare accessibility.

Malaria-Detection using VGG-19.

Malaria Detection - This Repository will help in differentiating between parasitized and non-parasitized malaria cells. Data is hosted at NIH's website as well as Kaggle(You can find the kaggle link in the README). This can be a starting point for understanding and implementing projects in CNN. The problem requires you to handle big data, perfor…

This project aims to automate malaria screening using computer aided diagnosis methods that includes machine learning (ML) and/or Convolutional Neural Network (CNN) techniques, applied to microscopic images of the smears.

Malaria Detection using Transfer Learning

Dielectrophoresis system simulation for malaria's detection.

Malaria is one of the disease causing deaths worldwide. Detection of malaria is a physical task invloving pathologist to diagnosis the presence of parasite inside the microscopic view of thin blood smears. This process is prone to errors as it requires an experienced person. Also, eye sight plays a vital role in order to detect the malaria. Ther…

Malaria parasites can be identified by examining under the microscope a drop of the patient's blood, spread out as a “blood smear” on a microscope slide. Prior to examination, the specimen is stained (most often with the Giemsa stain) to give the parasites a distinctive appearance.

It is a working Deep Learning model that can be used to predict whether a patient is infected with Malaria or not.

Deep learning classifier for diagnosis of malaria infection of blood cells. University project developed by Francesca Pezzuti and Pietro Tempesti for the Computational Intelligence and Deep Learning course.

Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the expe…