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 tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.

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. However, computed tomography (CT) and magnetic resonance imaging (MRI) scans can be used to help in AD diagnosis by observing critical changes in the size of different brain areas, typically parietal and temporal lobes areas. In this work, an integrative mulitresolutional ensemble deep learning-based framework is proposed to achieve better predictive performance for the diagnosis of Alzheimer disease. Unlike ResNet, DenseNet and their variants proposed pipeline utilizes PartialNet in a hierarchical design tailored to AD detection using brain MRIs. The advantage of the proposed analysis system is that PartialNet diversified the depth and deep supervision. Additionally, it also incorporates the properties of identity mappings which makes it powerful in better learning due to feature reuse. Besides, the proposed ensemble PartialNet is better in vanishing gradient, diminishing forward-flow with low number of parameters and better training time in comparison to its counter network. The proposed analysis pipeline has been tested and evaluated on benchmark ADNI dataset collected from 379 subjects patients. Quantitative validation of the obtained results documented our framework's capability, outperforming state-of-the-art learning approaches for both multi-and binary-class AD detection.

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. Unlike ResNet, We divided the network into several stages, and each stage consists of several dense connected residual units that support residual learning with dense connectivity and limited the skip connectivity. Thus, each stage can consider the features from its earlier layers locally as well as less complicated in comparison to its counter network. Evaluation results on ISIC-2018 challenge consisting of 10,015 training images show considerable improvement over other approaches achieving 98.05 percent accuracy and improving on the best results achieved in comparison to state of the art methods. The code of Unit-vise network is publicly available.1.

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.Bieb., Carthamus tinctorius L., and Beaumontia grandiflora Wall., utilizing light microscopy (LM) and scanning electron microscopy (SEM) for studying the detailed micromorphological features of these seeds. LM revealed that size ranges from 3 to 20 mm. furthermore, a great variety of color is observed from pitch black to greenish gray and yellowish white to off white. Seeds ultrastructure study with the help of SEM revealed a great variety in shape, size, color, sculpturing and periclinal wall shape, and so on. Followed by the production of fatty acid methyl esters from a novel source, that is, seeds oil of Brassica oleracea L. (seed oil content 42.20%, FFA content 0.329 mg KOH/g) using triple metal impregnated montmorillonite clay catalyst (Cu-Mg-Zn-Mmt). Catalyst was characterized using SEM-EDX, FT-IR. Maximum yield of Brassica oleracea L. biodiesel (87%) was obtained at the conditions; 1:9 of oil to methanol ratio, 0.5 g of catalyst, 5 hr reaction time, and 90°C of temperature. Synthesized biodiesel was characterized by FT-IR, GC-MS, and NMR. Fuel properties of the Brassica oleracea L. FAMES were determined and found in accordance with ASTM standards.

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., Capparis spinosa L., Physalis divaricata D.Don, Rosa moschata Herrm. and Ribes orientale Desf. collected from various localities of Pakistan. The objective of the present study is to investigate qualitative and quantitative anatomical characters for the identification and differentiation of collected wild edible fruits. The characters studied are shape and size of epidermal cells, anticlinal wall pattern, trichome type and shape, average number of stomata, length and width of stomata and pore. The detailed microscopic investigation and variations in the characters recorded have a key role in the determination and authentication of wild edible fruits. This study possesses great potential for plant taxonomists to further evaluate the species at molecular and genetic levels.

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. Exine sculpturing patterns (psilate, rugulate, scabrate, and microreticulate) were observed. The result indicated that exine ornamentation of Apocynaceae taxa is systematically informative at generic and species levels. Most of the species have tricolporate type pollen but tetraporate pollen was also observed in Trachelospermum jasminoides and tetracolpate in V. major. Minimum equatorial diameter was noted in Carissa edulis (27.13 µm) and maximum in V. major (108.25 µm). Similarly, maximum exine thickness was found in Cascabela thevetia (9.5 µm). In the present findings, the pollen morphological data are compared with available other pollen studies to evaluate the taxonomic value of pollen traits in Apocynaceae taxa by using multiple microscopic techniques. Furthermore, molecular and phylogenetic studies were recommended to strengthen the systematics of Apocynaceae taxa.

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. Nasta'liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition. We present Multi-dimensional Long Short Term Memory (MDLSTM) Recurrent Neural Networks with an output layer designed for sequence labeling for recognition of printed Urdu text-lines written in the Nasta'liq writing style. Experiments show that MDLSTM attained a recognition accuracy of 98% for the unconstrained Urdu Nasta'liq printed text, which significantly outperforms the state-of-the-art techniques.

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. However, leukocyte segmentation is a challenging process due to the complexity of the blood cell image; therefore, this task remains unresolved issue in the blood cell segmentation. OBJECTIVE: The aim of this work is to develop an efficient leukocyte cell segmentation and classification system. METHODS: This paper presents an efficient strategy to segment cell images. This has been achieved by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges. We have also used combination of entropy filter, thresholding and mathematical morphology for obtaining image segmentation and boundary detection, whereas we have used back-propagation neural network for leukocyte classification into its sub classes. RESULTS: As a result, the generated segmentation results are fruitful in a sense that we have overcome the problem of overlapping cells. We have obtained 100%, 96.15%, 92.30%, 92.30% and 96.15% accuracy for basophil, eosinophil, monocyte, lymphocyte and neutrophil respectively.

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. Therefore, these variations are not included in image databases and in experimental evaluations. This research uncovers challenges faced by Arabic cursive script recognition in a holistic framework by considering Pashto as a test case, because Pashto language has larger alphabet set than Arabic, Persian and Urdu. A database containing 8000 images of 1000 unique ligatures having scaling, orientation and location variations is introduced. In this article, a feature space based on scale invariant feature transform (SIFT) along with a segmentation framework has been proposed for overcoming the above mentioned challenges. The experimental results show a significantly improved performance of proposed scheme over traditional feature extraction techniques such as principal component analysis (PCA).

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. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm.