malaria-detection

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.

Web app for Malaria detection from the human blood sample images which is trained on National Library of Medicine dataset using Flask and Python.

An efficient disease detection application with GUI based (tkinter) frontend and a custom CNN model as backend which detects if a cell is parasitized or normal from its image in real time with an accuracy of 95.22%.

MEDINFORM - AI Powered Multipurpose Web platform for Medical Image Analysis

CenterNet for Malaria parasites detection

This project comprises predicting different types of disease at one place Pneumonia, Malaria, Liver Disease and Cardiovascular Disease

Using CNN to detect Malaria with the help of cell images

Malaria Detection Project on Malaria Cells

Malaria Cell Detection using Pytorch

Malaria Parasite Detection using Efficient Neural Ensembles. Malaria, a life threatening disease caused by the bite of the Anopheles mosquito infected with the parasite, has been a major burden towards healthcare for years leading to approximately 400,000 deaths globally every year. This study aims to build an efficient system by applying ensemble techniques based on deep learning to automate the detection of the parasite using whole slide images of thin blood smears.

Malaria Detection Web App.

A Sliding Window Approach for Malaria Detection in Thin Blood Film Images using Deep Learning

The project includes OpenVINO optimized image classification fast.ai model used to learn and classify healthy and infected blood smear malaria images.

A machine learning model and GUI for detecting Malaria in a cell.

A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

Using tensorflow to predict if a cell is infected by malaria.

All the projects in this repository are END to END in the sense projects are done from scratch from data collection to deployment of the deep learning models.

Convolutionnal Neural Network reaches 94% test accuracy on Malaria cell dataset (https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria)

Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells

This repository contains the projects/models that I have worked on during the course of my data science practice at UTS/Institute of Data.

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

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.

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 experience and well knowledgeable person needed for the diagnosis.

Healthcare Web App: Malaria Detection