Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs.

Tuberculosis (TB) is an infectious disease affecting humans' lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative for early TB screening. We propose an automatic TB detection system using advanced deep learning (DL) models.

Computational techniques for the automated detection of mycobacterium tuberculosis from digitized sputum smear microscopic images: A systematic review.

Background: Tuberculosis is an infectious disease that is caused by Mycobacterium tuberculosis (MTB), which mostly affects the lungs of humans. Bright-field microscopy and fluorescence microscopy are two major testing techniques used for tuberculosis (TB) detection. TB bacilli were identified and counted manually from sputum under a microscope and were found to be tedious, laborious and error prone.

Epigenetic repression of E-cadherin expression by hepatitis B virus x antigen in liver cancer.

Loss of E-cadherin is associated with acquisition of metastatic capacity. Numerous studies suggest that histone deacetylation and/or hypermethylation of CpG islands in E-cadherin gene (CDH1) are major mechanisms responsible for E-cadherin silencing in different tumors and cancer cell lines. The hepatitis B virus (HBV)-encoded X antigen, HBx, contributes importantly to the development of hepatocellular carcinoma using multiple mechanisms.