DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.

Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

Lung Cancer Diagnoses and Outcomes During the Syrian War, 2011-2018.

Importance: Protracted wars, which disproportionately affect low-resource countries, exacerbate the challenges faced by cancer care systems, with lung cancer being the most affected as the most fatal oncological condition. Objective: To characterize the presentation and survival of patients with lung cancer during the decade-long Syrian war. Design, Setting, and Participants: This cohort study included patients at a large oncology center in Syria during the first 8 years of the Syrian armed conflict (2011-2018). All patients with a new diagnosis of lung cancer were included. Variables investigated included histological subtypes, TNM classification (tumor, lymph nodes, and metastasis), and staging at diagnosis as well as the yearly follow-ups up to 5 years after presentation. Exposure: The Syrian war divided the country into temporary regions with different political influences and heterogeneously impacted health care accessibility among these regions. Main Outcomes and Measures: Change in prevalence of advanced lung cancer cases at presentation; change in overall survival odds. Results: The study included 5160 patients from all Syrian governorates (mean [SD] age, 59.6 [10.8] years; 4399 men [85.3%]). New diagnoses sharply declined during the first 3 years of the war, with partial recovery afterward. Regardless of their tempo-geographical regions, 60% to 80% of the yearly diagnoses presented with metastases. The 1-year and 5-year survival rates were 13.1% (423 of 3238 patients with follow-up results) and 0.1% (2 of 1853 patients), respectively. Patients who presented from regions more involved in the armed conflicts showed poorer survival rates with odds ranging between 0.51 (95% CI, 0.44-0.59) and 0.61 (95% CI, 0.49-0.74) across follow-ups for up to 2 years in comparison with patients presenting from safer regions. War-related health care inaccessibility explained a greater percentage of the variability in survival (Nagelkerke R2 = 7.5%; P < .001) than both patients' age and the stage of the disease combined (Nagelkerke R2 = 3.9%; P < .001). Conclusions and Relevance: The Syrian war has been associated with a marked decline in the management of patients with lung cancer, with limited access to specialized care, delayed diagnoses, and substantial decrease in survival rates correlating with the intensity of armed conflict in the respective regions. The profound repercussions of the prolonged armed conflict on patients with lung cancer in Syria necessitates urgent comprehensive strategies to improve the accessibility and quality of health care services, especially in conflict-ridden zones.

Third party monitoring for health in Afghanistan: the good, the bad and the ugly.

Third party monitoring (TPM) is used in development programming to assess deliverables in a contract relationship between purchasers (donors or government) and providers (non-governmental organisations or non-state entities). In this paper, we draw from our experience as public health professionals involved in implementing and monitoring the Basic Package of Health Services (BPHS) and the Essential Package of Hospital Services (EPHS) as part of the SEHAT and Sehatmandi programs in Afghanistan between 2013 and 2021. We analyse our own TPM experience through the lens of the three parties involved: the Ministry of Public Health; the service providers implementing the BPHS/EPHS; and the TPM agency responsible for monitoring the implementation. Despite the highly challenging and fragile context, our findings suggest that the consistent investments and strategic vision of donor programmes in Afghanistan over the past decades have led to a functioning and robust system to monitor the BPHS/EPHS implementation in Afghanistan. To maximise the efficiency, effectiveness and impact of this system, it is important to promote local ownership and use of the data, to balance the need for comprehensive information with the risk of jamming processes, and to address political economy dynamics in pay-for-performance schemes. Our findings are likely to be emblematic of TPM issues in other sectors and other fragile and conflicted affected settings and offer a range of lessons learnt to inform the implementation of TPM schemes.

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.

Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.

Lessons from the development process of the Afghanistan integrated package of essential health services.

In 2017, in the middle of the armed conflict with the Taliban, the Ministry of Public Health decided that the Afghan health system needed a well-defined priority package of health services taking into account the increasing burden of non-communicable diseases and injuries and benefiting from the latest evidence published by DCP3. This leads to a 2-year process involving data analysis, modelling and national consultations, which produce this Integrated Package of Essential health Services (IPEHS). The IPEHS was finalised just before the takeover by the Taliban and could not be implemented. The Afghanistan experience has highlighted the need to address not only the content of a more comprehensive benefit package, but also its implementation and financing. The IPEHS could be used as a basis to help professionals and the new authorities to define their priorities.

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector.

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

Multi-classification deep neural networks for identification of fish species using camera captured images.

Regular monitoring of the number of various fish species in a variety of habitats is essential for marine conservation efforts and marine biology research. To address the shortcomings of existing manual underwater video fish sampling methods, a plethora of computer-based techniques are proposed. However, there is no perfect approach for the automated identification and categorizing of fish species. This is primarily due to the difficulties inherent in capturing underwater videos, such as ambient changes in luminance, fish camouflage, dynamic environments, watercolor, poor resolution, shape variation of moving fish, and tiny differences between certain fish species. This study has proposed a novel Fish Detection Network (FD_Net) for the detection of nine different types of fish species using a camera-captured image that is based on the improved YOLOv7 algorithm by exchanging Darknet53 for MobileNetv3 and depthwise separable convolution for 3 x 3 filter size in the augmented feature extraction network bottleneck attention module (BNAM). The mean average precision (mAP) is 14.29% higher than it was in the initial version of YOLOv7. The network that is utilized in the method for the extraction of features is an improved version of DenseNet-169, and the loss function is an Arcface Loss. Widening the receptive field and improving the capability of feature extraction are achieved by incorporating dilated convolution into the dense block, removing the max-pooling layer from the trunk, and incorporating the BNAM into the dense block of the DenseNet-169 neural network. The results of several experiments comparisons and ablation experiments demonstrate that our proposed FD_Net has a higher detection mAP than YOLOv3, YOLOv3-TL, YOLOv3-BL, YOLOv4, YOLOv5, Faster-RCNN, and the most recent YOLOv7 model, and is more accurate for target fish species detection tasks in complex environments.

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.

Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19.

Revealing the significant shortcomings in the learning environment at the three largest medical schools in Syria: what's next?

BACKGROUND: Medical education in Syria still adopts a traditional, teacher-centered curriculum to this day. These elements imply the existence of issues in the learning environment (LE). This study aims to provide the first evaluation of the LE at the largest medical schools in Syria using the DREEM inventory. METHODS: The three largest medical schools in Syria are the ones at Damascus University (DU), University of Aleppo (AU), Tishreen University (TU). The Arabic version of the DREEM questionnaire was used. Students across all years of study except year 1 were approached. Both paper-based and electronic surveys were conducted. RESULTS: A total of 1774 questionnaire forms were completed (DU:941, AU:533, TU: 300). The overall DREEM score at DU, AU, and TU were 100.8 ± 28.7, 101.3 ± 31.7, and 97.8 ± 35.7 respectively with no significant difference (P = 0.254) between the three universities. DREEM subscales concerning Learning, Atmosphere, Academic Self-perception and Social Self-perception had a low score across all universities. Clinical-stage students reported significantly lower perception (P ≤ 0.001) of the LE in comparison to their pre-clinical counterparts across all subscales. CONCLUSIONS: The findings of this study highlight the significant shortcomings of the medical LE in Syria. If not addressed properly, the academic, clinical, and professional competence of the healthcare workforce will continue to deteriorate. Moreover, the negative LE might be a predisposing factor for medical students' exodus. The Syrian medical education system requires leaders who are willing to defy the status quo to achieve a true educational transformation.

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

Production of diamond using intense heavy ion beams at the FAIR facility and application to planetary physics.

Diamonds are supposedly abundantly present in different objects in the Universe including meteorites, carbon-rich stars as well as carbon-rich extrasolar planets. Moreover, the prediction that in deep layers of Uranus and Neptune, methane may undergo a process of phase separation into diamond and hydrogen, has been experimentally verified. In particular, high power lasers have been used to study this problem. It is therefore important from the point of view of astrophysics and planetary physics, to further study the production processes of diamond in the laboratory. In the present paper, we present numerical simulations of implosion of a solid carbon sample using an intense uranium beam that is to be delivered by the heavy ion synchrotron, SIS100, that is under construction at the Facility for Antiprotons and Ion Research (FAIR), at Darmstadt. These calculations show that using our proposed experimental scheme, one can generate the extreme pressure and temperature conditions, necessary to produce diamonds of mm3 dimensions.

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays.

Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19's medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar's, and ANOVA tests also showed the robustness of the proposed model.

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.

The development of Afghanistan's Integrated Package of Essential Health Services: Evidence, expertise and ethics in a priority setting process.

Health systems in fragile states need to respond to shifting demographics, burden of disease and socio-economic circumstances in the revision of their health service packages. This entails making difficult decisions about what is and is not included therein, especially in resource-constrained settings offering or striving for universal health coverage. In this paper we turn the lens on the 2017-2021 development of Afghanistan's Integrated Package of Essential Health Services (IPEHS) to analyse the dynamics of the priority setting process and the role and value of evidence. Using participant observation of meetings and interviews with 25 expert participants, we conducted a qualitative study of the consultation process aimed at examining the characteristics of its technical, socio-cultural and organisational aspects, in particular data use and expert input, and how they influenced how evidence was discussed, taken up, and used (or not used) in the process. Our analysis proposes that the particular dynamics shaped by the context, information landscape and expert input shaped and operationalized knowledge sharing and its application in such a way to constitute a sort of "vernacular evidence". Our findings underline the importance of paying attention to the constellation of the priority setting processes in order to contribute to an ethical allocation of resources, particularly in contexts of resource scarcity and humanitarian need.

Cross-polarized surface lattice resonances in a rectangular lattice plasmonic metasurface.

Multiresonant metasurfaces could enable many applications in filtering, sensing, and nonlinear optics. However, developing a metasurface with more than one high-quality-factor or high-Q resonance at designated resonant wavelengths is challenging. Here, we experimentally demonstrate a plasmonic metasurface exhibiting different, narrow surface lattice resonances by exploiting the polarization degree of freedom where different lattice modes propagate along different dimensions of the lattice. The surface consists of aluminum nanostructures in a rectangular periodic lattice. The resulting surface lattice resonances were measured around 640 nm and 1160 nm with Q factors of ∼50 and ∼800, respectively. The latter is a record-high plasmonic Q factor within the near-infrared type-II window. Such metasurfaces could benefit such applications as frequency conversion and all-optical switching.

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis.

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.

Protecting essential health services in low-income and middle-income countries and humanitarian settings while responding to the COVID-19 pandemic.

In health outcomes terms, the poorest countries stand to lose the most from these disruptions. In this paper, we make the case for a rational approach to public sector health spending and decision making during and in the early recovery phase of the COVID-19 pandemic. Based on ethics and equity principles, it is crucial to ensure that patients not infected by COVID-19 continue to get access to healthcare and that the services they need continue to be resourced. We present a list of 120 essential non-COVID-19 health interventions that were adapted from the model health benefit packages developed by the Disease Control Priorities project.