Image segmentation algorithms and techniques find its applications in a wide number of domains. Segmentation of brain tumor and overall internal structure of the brain is one of the main applications in the field of medical imaging. Magnetic resonance imaging (MRI) technique is one of the many imaging modalities that are available to scan and capture the internal soft tissue structures of the body. In this paper, proposed technique has been given to extract the tumor portion, successfully demarcate the tumor boundary, locate the tumor with a bounding circle and to diagnose whether the tumor is present or absent. A fuzzy clustering-based technique is proposed which helps to study & analyze the intricate structure of the brain, hence can be used as a visual analysis and a study tool. General TermsImage Processing, Pattern Recognition Keywords MRI, Magnetic resonance imaging, image segmentation, fuzzy clustering, thresholding INTRODUCTIONMagnetic resonance imaging (MRI) technique is one of the many available imaging modalities like CT-Scan, Mammography, and X-Ray. It provides visual details about the anatomy and the overall structure of the brain. An MRI scan can be used to study the supply of blood inside the brain. Hence MRI technology becomes an important tool for detecting abnormality, tracking the progress or growth of the disease and for diagnosis too. Processing digital images by means of a digital computer comprises digital image processing domain [1].Brain tumors are caused due to abnormal, uncontrolled growth of cells. Primary tumors are those that originate in the brain. Secondary tumors are those that originate in some other part of the body, finally reaching the brain through the process of metastasis.The symptoms of brain tumors include headache, nausea, vomiting, personality and behavioral changes, memory loss, sensory disturbance, weakness, numbness [2]. MRI ScanMRI is a fairly new technique that has been used since the beginning of the 1980s. The MRI scanner uses magnetic and radio waves to create pictures of tissues, organs and other structures within the body, which can then be viewed on a computer. There is no exposure to X-rays or any other damaging forms of radiation in MRI.The pictures produced by an MRI scan are better in displaying fine details and therefore are of higher diagnostic quality when compared to more frequently used X-ray scanners for example. Utility of MRI ImagesUsing an MRI scanner, it is possible to make pictures of almost all the tissue in the body. The tissue that has the least hydrogen atoms (such as bones) turns out dark, while the tissue that has many hydrogen atoms (such as fatty tissue) looks much brighter.By changing the timing of the radio wave pulses, it is possible to gain information about the different types of tissues that are present.An MRI of the brain and spinal cord can be performed to look at different abnormalities, as it can provide clear images of these structures in spite of being surrounded by bone tissue. Changes within the tissues of brain, ...
<span lang="EN-US">Fine-tuning of a model is a method that is most often required to cater to the users’ explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations are not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset while allowing the users to use their externally available images to test the model’s accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</span>
Peer-to-peer network principles are the foundation of Blockchain Cybersecurity. Blockchain creates a reliable verification method that protects against online dangers. Cryptocurrency on the Blockchain is supported by three pillars: network availability, secrecy, and integrity. A third route toward stronger security, one that is less traveled and not nearly as inviting to attackers, is provided by Blockchain. This method lessens risks, offers robust encryption, and more successfully confirms the ownership and integrity of data. Some passwords frequently referred to as the weakest link in Cybersecurity, may even be unnecessary without them. So we aim to build a secure user authentication system using blockchain and also learn about how SCADA systems work in healthcare.
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