Search results

One of the foremost research disciplines in medical image processing is to identify tumors, which is a challenging task practicing traditional methods. To overcome this, various research studies have been done effectively.

Medical image processing is evolving swiftly with modern technologies being developed every day. The advanced technologies improve medical fields in diagnosing diseases at the more advanced stages and serve to provide proper treatment.

Either the mass growth or abnormal growth concerning the cells in the brain is called a brain tumor.

The brain tumor can be categorized into two significant varieties, non-cancerous and cancerous. The carcinogenic tumors or cancerous is termed as malignant and non-carcinogenic tumors are termed benign tumors. If the cells in the tumor are healthy then it is a benign tumor, whereas, the abnormal growth or the uncontrollable growth of the cell is indicated as malignant. To find the tumor the magnetic resonance imaging (MRI) is carried out which is a tiresome and monotonous task done by a radiologist. In-order to diagnosis the brain tumor at the initial stage effectively with improved accuracy, the computer-aided robotic research technology is incorporated. There are numerous segmentation procedures, which help in identifying tumor cells from MRI images. It is necessary to select a proper segmentation mechanism to detect brain tumors effectively that can be aided with robotic systems. This research paper focuses on self-organizing map (SOM) by applying the adaptive network-based fuzzy inference system (ANFIS). The execution measures are determined to employ the confusion matrix, accuracy, sensitivity, and furthermore, specificity. The results achieved conclusively explicate that the proposed model presents more reliable outcomes when compared to existing techniques.

The purpose of this paper is to present the concept of institutions as compliant environments, using data to monitor and enforce compliance with a range of external policies and initiatives, using the particular example of UK higher education (HE) institutions. The paper differs from previous studies by bringing together a range of policies and uses of data covering different areas of HE and demonstrating how they contribute to the common goal of compliance.

The compliant environment is defined in this context and the author has applied the preliminary model to a range of policies and cases that use and reuse data from staff and students in HE.

The findings show that the focus on compliance with these policies and initiatives has resulted in a high level of surveillance of staff and students and a lack of resistance towards policies that work against the goals of education and academia.

This is the first study to bring together the range of areas in which policy compliance and data processing are entwined in HE. The study contributes to the academic literature on data and surveillance and on academic institutions as organisations.

The paper offers suggestions for resistance to compliance and data processing initiatives in HE.

This is the first study to bring together the range of areas in which policy compliance and data processing are entwined in HE. The study contributes to the academic literature on data and surveillance and on academic institutions as organisations.