Abstract:
Brain tumor diagnosis, a gradual process indulges several techniques; aid the classification of brain tumor. The diagnosis
procedure comprises, pre-processing, localization, feature extraction, segmentation and classification. Deep Learning (DL) algorithms
support every diagnosis and classification process. Depending on the dataset used, the authors decide the Machine Learning (ML)
algorithms either separately or fuse the algorithms and procedures to attain foolproof classification. The paper’s objective is to throw
light on the procedures adopted in brain tumor detection and classification processes. The paper focuses on the conservative and
contemporary approaches of the past two decades namely, (a) Threshold-based approaches, (b) Active-Contour Model-based
approaches, (c) Bounding Box-based approaches, (d) Clustering-based approaches, (e) Genetic Algorithm-based Clustering
approaches, (f) Texture-based Segmentation approaches, (g) Optimization-based approaches, (h) Phase Stretch Transform-based
approaches and Hybridized-conventional approaches for optimum performance. Apart from the procedures of the prevailing
algorithms, the performances of those methods were discussed in a precise manner, such as the dataset adopted, suitable ML models
with its architecture and distinct performance metrics along with their significance and pitfalls. To conclude, the findings of the existing
methods provide valuable insights for researchers in terms of research recommendations and opportunities for refinement, specifically
in relation to brain tumor processing stages.
