Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Conservative management of grade 1V renal injury with complete transection: a case report

The expectant management of high grade renal injuries in hemodynamically stable children has gained increasing acceptance amongst paediatric surgeons. However, patients with grade 1V injury with complete renal transection have been identified as a subgroup with a poor outcome that may benefit from early operative intervention

The filtering equations revisited

The problem of nonlinear filtering has engendered a surprising number of mathematical techniques for its treatment. A notable example is the change-of--probability-measure method originally introduced by Kallianpur and Striebel to derive the filtering equations and the Bayes-like formula that bears their names. More recent work, however, has generally preferred other methods. In this paper, we reconsider the change-of-measure approach to the derivation of the filtering equations and show that many of the technical conditions present in previous work can be relaxed. The filtering equations are established for general Markov signal processes that can be described by a martingale-problem formulation. Two specific applications are treated

Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock

A literature review on surgery for cervical vagal schwannomas

Cervical vagal schwannoma is a benign, slow-growing mass, often asymptomatic, with a very low lifetime risk of malignant transformation in general population, but diagnosis is still a challenge. Surgical resection is the treatment of choice even if its close relationship with nerve fibres, from which it arises, threats vagal nerve preservation. We present a case report and a systematic review of literature. All studies on surgical resection of cervical vagal schwannoma have been reviewed. Papers matching the inclusion criteria (topic on surgical removal of cervical vagal schwannoma, English language, full text available) were selected. Fifty-three patients with vagal neck schwannoma submitted to surgery were identified among 22 studies selected. Female/male ratio was 1.5 and median age 44 years. Median diameter was 5 cm (range 2 to 10). Most schwannoma were asymptomatic (68.2%) and received an intracapsular excision (64.9%). Postoperative symptoms were reported in 22.6% of patients. Cervical vagal schwannoma is a benign pathology requiring surgical excision, but frequently postoperative complications can affect patients lifelong, so, surgical indications should be based carefully on the balance between risks and benefits

Population Genetics of Franciscana Dolphins (Pontoporia blainvillei): Introducing a New Population from the Southern Edge of Their Distribution

Due to anthropogenic factors, the franciscana dolphin, Pontoporia blainvillei, is the most threatened small cetacean on the Atlantic coast of South America. Four Franciscana Management Areas have been proposed: Espiritu Santo to Rio de Janeiro (FMA I), São Paulo to Santa Catarina (FMA II), Rio Grande do Sul to Uruguay (FMA III), and Argentina (FMA IV). Further genetic studies distinguished additional populations within these FMAs. We analyzed the population structure, phylogeography, and demographic history in the southernmost portion of the species range. From the analysis of mitochondrial DNA control region sequences, 5 novel haplotypes were found, totalizing 60 haplotypes for the entire distribution range. The haplotype network did not show an apparent phylogeographical signal for the southern FMAs. Two populations were identified: Monte Hermoso (MH) and Necochea (NC)+Claromecó (CL)+Río Negro (RN). The low levels of genetic variability, the relative constant size over time, and the low levels of gene flow may indicate that MH has been colonized by a few maternal lineages and became isolated from geographically close populations. The apparent increase in NC+CL+RN size would be consistent with the higher genetic variability found, since genetic diversity is generally higher in older and expanding populations. Additionally, RN may have experienced a recent split from CL and NC; current high levels of gene flow may be occurring between the latter ones. FMA IV would comprise four franciscana dolphin populations: Samborombón West+Samborombón South, Cabo San Antonio+Buenos Aires East, NC+CL+Buenos Aires Southwest+RN and MH. Results achieved in this study need to be taken into account in order to ensure the long-term survival of the species.Fil: Gariboldi, María Constanza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico; ArgentinaFil: Tunez, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján; ArgentinaFil: Dejean, Cristina Beatriz. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico; Argentina. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Ciencias Antropológicas. Sección Antropología Biológica; ArgentinaFil: Failla, Mauricio. Fundación Cethus; ArgentinaFil: Vitullo, Alfredo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico; ArgentinaFil: Negri, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; ArgentinaFil: Cappozzo, Humberto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; Argentin

Effects of person-centered care approaches to dementia care on staff: a systematic review

YesPerson-centered care (PCC) has been the subject of several intervention studies, reporting positive effects on people with dementia. However, its impact on staff’s outcomes remains unclear. The purpose of this systematic review was to assess the impact of PCC approaches on stress, burnout and job satisfaction of staff caring for people with dementia in care homes. The databases PubMed, Web of Knowledge, Scopus and EBSCO and reference lists from relevant publications, were searched between December 2012 and March 2013. The review was limited to experimental and quasi-experimental studies, published in English and involving direct care workers (DCWs). Seven studies were included, addressing different PCC approaches: dementia care mapping (n=1); stimulation-oriented approaches (n=2); emotion-oriented approaches (n=2) and behavioral-oriented approaches (n=2). Five studies reported benefits on DCWs, suggesting a tendency towards the effectiveness of PCC on staff. However, methodological weaknesses and heterogeneity among studies make it difficult to draw firm conclusions.Portuguese Foundation for Science and Technolog

The effectiveness of public health interventions to reduce the health impact of climate change:a systematic review of systematic reviews

Climate change is likely to be one of the most important threats to public health in the coming years. Yet despite the large number of papers considering the health impact of climate change, few have considered what public health interventions may be of most value in reducing the disease burden. We aimed to evaluate the effectiveness of public health interventions to reduce the disease burden of high priority climate sensitive diseases

A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya

, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools.Here we present a logistic regression model of tsetse abundance. The goal of this model is to estimate the distribution of tsetse fly in Kenya in the year 2000, and to provide a method by which to anticipate their future distribution. Multiple predictor variables were tested for significance and for predictive power; ultimately, a parsimonious subset of variables was identified and used to construct the regression model with the 1973 tsetse map. These data were validated against year 2000 Food and Agriculture Organization (FAO) estimates. Mapcurves Goodness-Of-Fit scores were used to evaluate the modeled fly distribution against FAO estimates and against 1973 presence/absence data, each driven by appropriate climate data.Logistic regression can be effectively used to produce a model that projects fly abundance under elevated greenhouse gas scenarios. This model identifies potential areas for tsetse abandonment and expansion