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

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.

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 ensemb…

SANUS - A CADx Platform. To detect diseases with medical records.

MEDINFORM - AI Powered Multipurpose Web platform for Medical Image Analysis

Malaria Detection Project on Malaria Cells

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

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.

Using CNN to detect Malaria with the help of cell images

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

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

This repository contains code for a malaria detection system using a pre-trained ResNet50 model on TensorFlow. The model is trained to detect malaria parasites in cell images.

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…

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 accurat…

疟疾检测 Malaria detection

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…