Topic: MRI scans

Introduction: Early diagnosis and classification of brain tumours are crucial to enable decisive early treatment and improve patient outcomes. Misdiagnosed brain malignancies can result in incorrect medical interventions, reducing patient survival. Computer-aided diagnostic systems incorporated with artificial intelligence techniques can be used as a robust clinical decision support system to improve brain tumour diagnosis. Aim: We aim to develop an artificial intelligence algorithm to classify MRI images into different brain tumours. Methodology: We used 7022 T1-weighted MRI scans compri

Robust detection of biological structures such as neuronal dendrites in brightfield micrographs, tumor tissue in histological slides, or pathological brain regions in MRI scans is a fundamental task in bio-image analysis. Detection of those structures requests complex decision making which is often impossible with current image analysis software, and therefore typically executed by humans in a tedious and time-consuming manual procedure. Supervised pixel classification based on Deep Convolutional Neural Networks (DNNs) is currently emerging as the most promising technique to solve such complex