Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images

Tuberculosis, caused by a bacteria called Mycobacterium tuberculosis, is one of the most serious public health problems worldwide. This work seeks to facilitate and automate the prediction of tuberculosis by the MODS method and using lens-free microscopy, which is easy to use by untrained personnel. We employ the CapsNet architecture in our collected dataset and show that it has a better accuracy than traditional CNN architectures.

READ FULL TEXT
research
07/06/2020

Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images

Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, i...
research
04/02/2021

Prediction of Tuberculosis using U-Net and segmentation techniques

One of the most serious public health problems in Peru and worldwide is ...
research
11/25/2020

Single-Image Lens Flare Removal

Lens flare is a common artifact in photographs occurring when the camera...
research
09/06/2022

Optimal design of photonic nanojets under uncertainty

Photonic nanojets (PNJs) have promising applications as optical probes i...
research
01/10/2020

Diagnosing Colorectal Polyps in the Wild with Capsule Networks

Colorectal cancer, largely arising from precursor lesions called polyps,...
research
07/02/2021

LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos

A critical complication after cataract surgery is the dislocation of the...
research
05/04/2018

Automatic Estimation of Modulation Transfer Functions

The modulation transfer function (MTF) is widely used to characterise th...

Please sign up or login with your details

Forgot password? Click here to reset