A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems

Abstract

PurposeOne 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.Design/methodology/approachMedical 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.FindingsEither the mass growth or abnormal growth concerning the cells in the brain is called a brain tumor.Originality/valueThe 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.

Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR) in WSNs

“…The stochastic sleep scheduling model is given for node selection process, considering the range of network coverage. For providing energy efficiency and distribution of CH nodes, heuristic crossover operators are used [24,25].…”