Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases

Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists.  In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc. Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores. Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely. Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease

Morphological characterization of a polymeric microfiltration membrane by synchrotron radiation computed microtomography

Most commercial polymeric membranes are prepared by phase inversion. The performance of the membranes depends greatly on the morphology of the porous structure formed during the different steps of this process. Researchers in this field have found it extremely difficult to foresee how a change in the composition of the polymer solution will affect pore formation without a set of methods designed to yield detailed knowledge of the morphological structure. This paper reports the new potential associated with X-Ray synchrotron microtomography to characterize the 3D structure of a PvDF hollow fibre microfiltration membrane prepared by phase inversion. 3D morphological data obtained from the ID19 line at the ESRF are presented. The membrane actually appears as a complex three-dimensional bi-continuum of interconnected pores. Within the hollow fibre structure, different regions with various thicknesses and pore size distributions have been identified and well characterized. Transversal views show the anisotropic finger-like structure of pores, while longitudinal sections reveal a honeycomb structure which resembles the structure of highly concentrated water in oil emulsion or dispersion. This typical structure might be obtained during the phase inversion process. How the phase inversion process may result in these morphologies is finally discussed

A comparative study of techniques used for porous membrane characterization: pore characterization

A range of commerical UF membranes have been characterized by thermoporometry, biliquid permporometry and molecular weight cut-off experiments. A comparison of results from these three independent techniques for the same types of membrane shows an indication of the strength and weakness of the methods. MWCO values determined from actual rejection values using PEG and dextran were significantly lower than the manufacturer supplied data. The data obtained using the biliquid permporometry and solute rejection tests produced contrasting results for Amicon polysulfone (PM30) and regenerated cellulose (YM30) membranes. While MWCO determination resulted in sharper cut-off curves, the biliquid permporometry offered a broader size distribution with the PM30 and vice versa with the YM30. The pore sizes obtained by thermoporometry were significantly larger than those by the biliquid permporometry. The biliquid permporometry and thermoporometry give significantly higher values than the MWCO method. The closest comparison is obtained between the EM values and the MWCO method. This suggests that the controlling pore dimension for separation is the surface skin dimension

3D body scanning and healthcare applications

Developed largely for the clothing industry, 3D body-surface scanners are transforming our ability to accurately measure and visualize a person's body size, shape, and skin-surface area. Advancements in 3D whole-body scanning seem to offer even greater potential for healthcare applications

Macroporous materials: microfluidic fabrication, functionalization and applications

This article provides an up-to-date highly comprehensive overview (594 references) on the state of the art of the synthesis and design of macroporous materials using microfluidics and their applications in different fields

Novel active sweat pores based liveness detection techniques for fingerprint biometrics

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.This study is funded by the University of Sindh, Jamshoro, Pakistan and the Higher Education Commission of Pakistan

A Review of Skin Melanoma Detection Based on Machine Learning

Dermatological malignancies, such as skin cancer, are the most extensively known kinds of human malignancies in people with fair skin. Despite the fact that malignant melanoma is the type of skin cancer that is associated with the highest mortality rate, the non-melanoma skin tumors are unquestionably normal. The frequency of both melanoma and non-melanoma skin cancers is increasing, and the number of cases being studied is increasing at a reasonably regular period, according to the National Cancer Institute. Early detection of skin cancer can help patient’s live longer lives by reducing their mortality rate. In this research, we will look at various approaches for initiating period melanoma skin cancer detection and compare them. Pathologists use biopsies to diagnose skin lesions, and they base their decisions on cell life systems and tissue transport in many cases. However, in many cases, the decision is emotional, and it commonly results in significant changeability. The application of quantitative measures by PC diagnostic devices, on the other hand, allows for more accurate target judgment. This research examines the preceding period as well as current advancements in the field of machine-aided skin cancer detection (MASCD)