Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis.

Identifying defective solar cells in electroluminescence images using deep feature representations.

Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise.