A Study on Brain Tumor and Parkinson’s Disease Diagnosis and Detection using Deep Learning

Abstract

Consider the possibility that we live in an area far from a doctor, or that we may not have enough resources to pay the hospital cost, or that we may not have enough time to take off work. The use of advanced computers to diagnose diseases will be lifesaving in such situations. Scientists have developed a number of artificially intelligent diagnostic algorithms for illnesses such as cancer, lung disease and Parkinson's disease. Deep learning employs massive artificial neural network layers of interlinked nodes that can reorganize themselves in response to updated data. This approach enables machines to self-learn without the need for assistance from humans. The emphasis of this article is on current developments in machine learning that have had major effects on identification for the detection of a variety of illnesses, such as brain tumor segmentation. Human-assisted manual categorization may lead to erroneous prediction and diagnosis, thus one of the most important and a useful technique is brain tumor segmentation tasks in medical image processing that are difficult. Furthermore, it is a difficult challenge because there is a vast volume of data to assist. Since brain tumors have such a wide range of appearances and since tumor and normal tissues are so close, extracting tumor regions from photographs becomes difficult. The advancement of clinical decision systems of support necessitates the identification and recognition of the appropriate biomarkers in relation to specific health problems. It has been established that handwriting deficiency is proportionate to the severity of the situation of individuals' Parkinson's disease (PD).