Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …

In this paper, we present a fully automatic brain tumor segmentation and classification model
using a Deep Convolutional Neural Network that includes a multiscale approach. One of the …

The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …

The review covers automatic segmentation of images by means of deep learning
approaches in the area of medical imaging. Current developments in machine learning …

Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Background Detecting brain tumors in their early stages is crucial. Brain tumors are
classified by biopsy, which can only be performed through definitive brain surgery …

Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

Radiology is a broad subject that needs more knowledge and understanding of medical
science to identify tumors accurately. The need for a tumor detection program, thus …

Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …