Classification of skin cancer from dermoscopic images using deep neural network architectures.

A powerful medical decision support system for classifying skin lesions from dermoscopic images is an important tool to prognosis of skin cancer. In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images, in-spite of its fine grained variability in its appearance. The main objective of this research work is to develop a DCNN based model to automatically classify skin cancer types into melanoma and non-melanoma with high accuracy.

A computational model for enhancing recombinant Penicillin G Acylase production from Escherichia coli DH5α.

An attempt was made to develop a computational model based on artificial neural network and ant colony optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase (PGA) enzyme from Escherichia coli DH5α strain harboring the plasmid pPROPAC. As a first step, an artificial neural network (ANN) model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium. Design of experiments employing central composite design technique was used to obtain the training samples.

Extrauterine Intrapartum Treatment (EXIT) in bilateral primary fetal hydrothorax.

Primary Foetal Hydrothorax (PFHT), is an intrathoracic collection of fluid in the fetus, which may be present on either side or even bilaterally. Advances in foetal diagnostics now allow consideration of the Ex-utero Intrapartum Treatment (EXIT) procedure for PFHT. Ex-utero Intrapartum Treatment (EXIT) allows therapeutic interventions on the neonate while maintaining fetoplacental circulation and thereby maintaining oxygenation.