/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical Image Analysis using Convolutional Neural Networks: A Review

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.

https://www.mdpi.com/1424-8220/12/1/55/pdf

Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

In the recent development of, Internet of Things (IoT) makes all objects interconnected and it has been recognized as the next technical revolution Some of the applications of Internet of Things are smart parking, smart home, smart city, smart environment, industrial places, agriculture fields and health monitoring process One such application is in healthcare to monitor the patient health status Internet of Things makes medical equipments more efficient by allowing real time monitoring of patient health, in which sensor acquire data of patient's and reduces the human error In Internet of Things patient's parameters get transmitted through medical devices via a gateway, where it is stored and analyzed The significant challenges in the implementation of Internet of Things for healthcare applications is monitoring all patient's from various places Thus Internet o Things in the medical field brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management In this paper discuss about, monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board

An IoT based patient monitoring system using raspberry Pi

Novel SOM based FKM algorithm for tissue segmentation and tumor identification in magnetic resonance brain images (T1-w, T2-w, FLAIR and MPR sequences) is proposed through this work.Exact demarcation between tumor and edema region is characterized.Validation of the segmented results by an experienced radiologist.Cross comparison with FCM, SOM, FKM and other hybrid clustering algorithms using ten standard comparison parameters. Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository.

An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Nowadays, Health care is considered as an important aspect of our everyday lives. To address the emerging health-related challenges, this research wprk has utilised the advanced digital technologies to develop a health tracking analyzer. Recently, the use of mobile phones and other gadgets has increased unprecedentedly by creating a significant impact on the mobile health care. Mobile Health applications serve a large number of consumers. The main aim of this research work is to create a health tracking analyzer so that any patient or anybody who is concerned about their health may be readily tracked by their doctors or health care providers. This loT -based device aids in critical situations by avoiding illness transmission and providing health diagnoses even when the doctor is at a considerable distance away from the patient. Many people are receiving therapy for a variety of issues, including heart disease. The proposed model plays a vital role in checking and storing the healthcare-related data in server by using a Wi-Fi Module. Furthermore, the proposed device tracks the heartbeat and temperature by using the detectors and also a microcontroller is attached to the sensor. To frequently update the condition of patient's, the microcontroller is linked with LCD display and Wi-Fi to transfer the information to the database. By employing an inter connection, the pulse rate and temperature can also be analyzed. The gadget will exchange the readings from the sensor to the cloud and the information gathered will also remain accessible. The proposed database analyzes the received information and alerts the patient through buzzer.

Indigenous Health Tracking Analyzer Using IoT

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

The Discrete Wavelet transform is one of the best tools for signal and data analysis, It requires efficient hardware implementation in the real-time applications. The submissions established in the field of imaging necessitates compacted architecture. In DWT discrete sampling is accomplished for the wavelets. In this paper, the VLSI implementation of HAAR wavelet-based image compression is proposed and designed. HAAR wavelet transform is one of the easiest methods for image compression because it has coefficients as either 1 or −1. In this work software alone is used for the compression together with optimizing it with a continuous optimization algorithm and provides a hardware-free architecture with low cost. The VHDL work is carried out in Xilinx Platform and provides a truncated power architecture for a concrete application. The same VHDL architecture can also be instigated in FPGA which will harvest hardware effectual compromising outcomes.

M. Pallikonda Rajasekaran

Papers

An IoT based patient monitoring system using raspberry Pi

An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Indigenous Health Tracking Analyzer Using IoT

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

VLSI Implementation of Image Compression using TSA Optimized Discrete Wavelet Transform Techniques