Review on chest pathogies detection systems using deep learning techniques.

Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated.

A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD worsens over time and greatly impacts patients' life and affects their important mental functions, including thinking, the ability to carry on a conversation, and judgment and response to environment. Clinically, there is no single test to effectively diagnose Alzheimer disease.

Unit-Vise: Deep Shallow Unit-Vise Residual Neural Networks With Transition Layer For Expert Level Skin Cancer Classification.

Many modern neural network architectures with over parameterized regime have been used for identification of skin cancer. Recent work showed that network, where the hidden units are polynomially smaller in size, showed better performance than overparameterized models. Hence, in this paper, we present multistage unit-vise deep dense residual network with transition and additional supervision blocks that enforces the shorter connections resulting in better feature representation.

Implication, visualization, and characterization through scanning electron microscopy as a tool to identify nonedible oil seeds.

Second-generation biofuels prove to be a distinctive and renewable source of sustainable energy and cleaner environment. The current study focuses on the exploration and identification of four nonedible sources, that is, Brassica oleracea L., Carthamus oxyacantha M.

Foliar epidermal anatomy of some selected wild edible fruits of Pakistan using light microscopy and scanning electron microscopy.

The present study is focused on the detailed foliar epidermal anatomy of some selected wild edible fruits (WEFs) from Pakistan using light microscopy (LM) and scanning electron microscopy (SEM). The studied species are Ficus racemosa L., Solanum nigrum L.

Palynological investigation of lactiferous flora (Apocynaceae) of District Rawalpindi, Pakistan, using light and scanning electron microscopy.

This was the first comprehensive pollen micromorphological investigation of lactiferous flora (Apocynaceae) of District Rawalpindi, Pakistan. The pollen morphology of 10 species of the family Apocynaceae was observed and documented using light microscopy and scanning electron microscopy. Pollen was found subspheroidal in shape in most of the species, however peroblate shape was noted in Vinca major.

Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks.

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style.

Efficient leukocyte segmentation and recognition in peripheral blood image.

Background: Blood cell count, also known as differential count of various types of blood cells, provides valuable information in order to assess variety of diseases like AIDS, leukemia and blood cancer. Manual techniques are still used in diseases diagnosis that is very lingering and tedious process. However, machine based automatic analysis of leukocyte is a powerful tool that could reduce the human errors, improve the accuracy, and minimize the required time for blood cell analysis.

Robust Optical Recognition of Cursive Pashto Script Using Scale, Rotation and Location Invariant Approach.

The presence of a large number of unique shapes called ligatures in cursive languages, along with variations due to scaling, orientation and location provides one of the most challenging pattern recognition problems. Recognition of the large number of ligatures is often a complicated task in oriental languages such as Pashto, Urdu, Persian and Arabic. Research on cursive script recognition often ignores the fact that scaling, orientation, location and font variations are common in printed cursive text.

Curvelet based offline analysis of SEM images.

Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz.