A novel approach toward skin cancer classification through fused deep features and neutrosophic environment

Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features' descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC's coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested—Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152—with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.

A Survey on Human Cancer Categorization Based on Deep Learning

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Parenchymal transection of the kidney inflicted by endocatch bag entrapment during a laparoscopic donor nephrectomy

We present a case of inadvertent decapsulation and grade IV renal parenchyma laceration during laparoscopic donor nephrectomy. The kidney was repaired, used and functioned immediately. There were no complications in the donor. To our knowledge, this type of injury has not been reported previously and the purpose of this report is to focus attention on the potential for this unusual injury, which can occur during delivery of the kidney using the endocatch bag.

Further improvements in laparoscopic donor nephrectomy: decreased pain and accelerated recovery

Fear of postoperative pain is a disincentive to living donor kidney transplantation. Laparoscopic donor nephrectomy (LDN) was developed in part to dispel this disincentive. The dramatic increase in the number of laparoscopic donor nephrectomies performed at our institution has been in part due to the reduction in postoperative pain as compared to traditional, open donor nephrectomy. We sought to further diminish the pain associated with this surgical technique. The purpose of this study was to compare the efficacy of three different postoperative pain management regimens after LDN. All living kidney donors performed laparoscopically (n=43) between September 1998 and April 2000 were included for analysis. Primary endpoints included postoperative narcotic requirements and length of stay. Narcotic usage was converted to morphine equivalents (ME) for comparison purposes. Patients received one of three pain control regimens (group 1: oral and intravenous narcotics; group II: oral and intravenous narcotics and the On-Q pump delivering a continuous infusion of subfascial bupivicaine 0.5%; and group III: oral and intravenous narcotics and subfascial bupivicaine 0.5% injection). Postoperative intravenous and oral narcotic use as measured in morphine equivalents was significantly less in group III versus groups I and II (group III: 28.7 ME versus group I: 40.2 ME, group II: 44.8 ME; P<0.05). Postoperative length of stay was also shorter for group III (1.8 days) versus group I (2.5 days) and group II (2.9 days). LDN has been shown to be a viable alternative to traditional open donor nephrectomy for living kidney donation. We observed that the use of combined oral and intravenous narcotics alone is associated with greater postoperative narcotic use and increased length of stay compared to either a combined oral and intravenous narcotics plus continuous or single injection subfascial administration of bupivicaine. The progressive modification of our analgesic regimen has resulted in decreased postoperative oral and intravenous narcotic use and a reduction in the length of stay. We recommend subfascial infiltration with bupivicaine to the three laparoscopic sites and the pfannenstiel incision at the conclusion of the procedure to reduce postoperative pain. We believe this improvement in postoperative pain management will continue to make LDN even more appealing to the potential living kidney donor.

Use of autologous radial artery for revascularization of hepatic artery thrombosis after orthotopic liver transplantation: case report and review of indications and options for urgent hepatic artery reconstruction

Hepatic artery thrombosis (HAT) is the most common vascular complication after orthotopic liver transplantation (OLT) and has traditionally been managed with re-OLT. However, several reports have shown that urgent revascularization is frequently an effective means of graft salvage. This most often involves hepatic artery (HA) thrombectomy and thrombolysis, with reestablishment of arterial inflow through a donor iliac artery conduit based on the supraceliac or infrarenal aorta. We report a 46-year-old man who developed HAT 13 days after OLT after angiographic splenic artery embolization to reduce splenic artery steal. A suitable donor iliac artery was not available for arterial reconstruction and could not be obtained from neighboring transplant centers. The patient underwent urgent HA thrombectomy, intrahepatic arterial thrombolysis, and revascularization using an autologous radial artery (RA) conduit based on the supraceliac aorta. The patient is alive more than 1 year after revascularization, with normal liver function and documented flow in the arterial conduit by Doppler ultrasound and arteriography. He has not developed late biliary complications or adverse sequelae of RA harvest. Autologous RA can be safely and successfully used as an aortic-based arterial conduit in urgent revascularization of HAT after OLT. RA should be considered for use in HA revascularization if an adequate donor iliac artery is not available and other potential conduits are not usable or desirable. The availability of autologous RA expands the armamentarium of vascular conduits that can be used in HA revascularization and may help minimize re-OLT for otherwise potentially salvageable liver allografts.