My Account | Careers | Downloads | Blog

PROJECT TITLE :

Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis

ABSTRACT:

Tuberculosis (TB) may be a common disease with high mortality and morbidity rates worldwide. Automatic systems to detect TB on chest radiographs (CXRs) can improve the potency of diagnostic algorithms for pulmonary TB. The numerous manifestation of TB on CXRs from totally different populations needs a system that can be custom-made to accommodate different varieties of abnormalities. A laptop aided detection (CAD) system was developed that combines many subscores of supervised subsystems detecting textural, form, and focal abnormalities into one TB score. A general framework was developed to mix an arbitrary variety of subscores: subscores were normalized, collected in a feature vector and then combined using a supervised classifier into one combined score. The method was evaluated on 2 databases, both consisting of two hundred digital CXRs, from: (A) Western high-risk group screening, (B) TB suspect screening in Africa. The subscores and combined score were compared to (1) an external, non-radiological, reference and (two) a radiological reference determined by a person's expert. Performance was measured using Receiver Operator Characteristic (ROC) analysis. Completely different subscores performed best in the two databases. The combined TB score performed better than the individual subscores, aside from the external reference in database B. The performances of the freelance observer were slightly on top of the combined TB score. Compared to the external reference, variations in performance between the combined TB score and therefore the freelance observer were not significant in each databases. Supervised combination to compute an overall TB score allows for a necessary adaptation of the CAD system to completely different settings or totally different operational requirements.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : T-PAIR Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation ABSTRACT: In this paper, we investigate a problem known as automatic hypothesis generation (HG). HG refers to the process of discovering
PROJECT TITLE : End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning ABSTRACT: Objective. The mean intracranial pressure, also known as ICP, is a measurement that is frequently
PROJECT TITLE : End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning ABSTRACT: Objective. The mean intracranial pressure, also known as ICP, is a measurement that is
PROJECT TITLE : Attention in Reasoning Dataset, Analysis, and Modeling ABSTRACT: Although attention has become an increasingly popular component in deep neural networks for the purpose of both interpreting data and improving
PROJECT TITLE : An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection ABSTRACT: We present a novel deep learning framework that we call the Iteratively Optimized Patch Label Inference

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry