Online ISSN: 2515-8260

Keywords : Texture

Integrated Deep Learning Model with Hybrid Texture based Me di c a l Image Retrieval System

Dr. A. Jayachandran, Dr.G.Shanmugarathinam

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 1, Pages 2408-2418

Electronic restorative imaging and examination techniques utilizing different modalities have
encouraged early determination. The development of the computer-aided retrivel systems in
recent years turned them into a nondestructive and popular method for diagnosis the disease
in medical images. In this work, adaptive Gabor wavelet filter bank and Texton based a
feature descriptor is developed for medical image retrieval. The design of the proposed
descriptor basis provides flexibility in order to extract the dominant directional features from
medical images.. Also, we present a novel end-to-end integrated deep learning model using
Convolutional Neural Network (CNN) and the Long Short-Term Memory cell (LSTM). The
proposed integrate deep learning descriptor is compared to other descriptor such as CCM,
CHD, MTH and MSD using the datasets such as New Caltech , Corel-1000,Oliva and Corel-

Sorption Characteristics Of The Mesoporous Sorbents Based On Tetraethoxysilane And Titanium Oxide

J.R. Uzokov; N.K. Mukhamadiev

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 656-660

Samples of mesoporous sorbents based on tetraethoxysilane and titanium dioxide in an acidic medium were obtained using the sol-gel technology. The synthesized mesoporous sorbents are characterized by a large specific surface area of 700-950 m2 / g, as well as an average diameter of 2.6-6.5 nm and a low pore volume of 0.60-0.90 cm3 / g. It was found that a hysteresis loop is observed in the mesoporous sorbents during the adsorption of benzene and water vapors. The phase composition of the sorbents was studied by X-ray diffractometry (XRD), surface characteristics by a scanning electron microscope (SEM). The mesoporous sorbents can be used as a sorbent for gas chromatographic separation of light alkanes, olefins, aromatic hydrocarbons, alcohols, aldehydes, ketones, ethers and esters.