Mahendrawarman's Stars
ncoudray/DeepPATH
Classification of Lung cancer slide images using deep-learning
11fenil11/Covid19-Detection-Using-Chest-X-Ray
Covid-19 detection in chest x-ray images using Convolution Neural Network.
THEGURUJ1/AI-for-Healthcare-Project-using-NVIDIA-Jetson-Nano-2GB-Developer-kit
This project uses Deep learning concept in detection of Various Deadly diseases. It can Detect 1) Lung Cancer 2) Covid-19 3)Tuberculosis 4) Pneumonia. It uses CT-Scan and X-ray Images of chest/lung in detecting the disease. It has a Accuracy between 50%-80%. It can take input in any Image format or through Live videos and provide accurate output results.
zekaouinoureddine/COVID-19-Detection-From-X-Ray
COVID-19 Detection From X-ray Images Using Deep Learning
BitterOcean/Covid19-Detector
"Covid19-Detector" is a Django-ReactJS Web App with an Artificial Intelligence. It can detect COVID-19 from CT Scan Images using CNN based on DenseNet121 architecture.
aneesh5/Lung-cancer-Detection
Detecting Pneumonia using AI
KaranPatel20/COVID-Pneumonia-LungCancer-Classification
RohanKittu/Pneumonia-Detection_tensorflow
What is Pneumonia? Pneumonia is an infection in one or both lungs. Bacteria, viruses, and fungi cause it. The infection causes inflammation in the air sacs in your lungs, which are called alveoli. Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2017, 920,000 children under the age of 5 died from the disease. It requires review of a chest radiograph (CXR) by highly trained specialists and confirmation through clinical history, vital signs and laboratory exams. Pneumonia usually manifests as an area or areas of increased opacity on CXR. However, the diagnosis of pneumonia on CXR is complicated because of a number of other conditions in the lungs such as fluid overload (pulmonary edema), bleeding, volume loss (atelectasis or collapse), lung cancer, or post- radiation or surgical changes. Outside of the lungs, fluid in the pleural space (pleural effusion) also appears as increased opacity on CXR. When available, comparison of CXRs of the patient taken at different time points and correlation with clinical symptoms and history are helpful in making the diagnosis. CXRs are the most commonly performed diagnostic imaging study. A number of factors such as positioning of the patient and depth of inspiration can alter the appearance of the CXR, complicating interpretation further. In addition, clinicians are faced with reading high volumes of images every shift. Pneumonia Detection Now to detection Pneumonia we need to detect Inflammation of the lungs. In this project, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.