**4. Conclusions**

In this work, a hybrid procedure is implemented, which uses fuzzy clustering with silhouette analysis followed by MCW, RG, and DRLS procedures. Moreover, this proposed method applied to the entire slices of abnormal patient studies obtained from the BRATS challenge and the Proscans Diagnostics Centre. This investigation delivered better segmentation of the regions where the concentration of tumor was high. The best-segmented objects are obtained using clustering techniques which are further evaluated by silhouette metrics. The tumor objects from the enhanced slices are segmented based on MCW/RG/DRLS techniques. The quantification results of the mined anomalies ensure the progression of counterpart tumors at di fferent treatment stages. The clinical significance of the proposed hybrid approach gives a better prognosis identification against the ground truth. The use of python open source technologies in implementing the work can visualize, analyze and interact with the slice data claim to be cost-e ffective. Hence, the proposed framework on MR DICOM slices requires less user intervention in extracting tumor heterogeneity from typical brain structures. Quantification and 3D modeling procedure help in finding a spatial identity and tumor concentration. By knowing the size, shape and spatial location of the tumor, the process of treating the tumor might be improved. The future work could include the implementation of advanced artificial intelligence methodologies for early, e fficient, and real-time diagnosis of malignant brain tumors [58–65].

**Supplementary Materials:** The video abstract can be found at the following link: https://drive.google.com/file/ d/1ddr0DxPNP1cX7aMC-dvSx12sQJ4fuH59/view?ts=5e452180. Video: An E fficient Hybrid Fuzzy-Clustering Driven 3D-Modeling.

**Author Contributions:** Conceptualization, S.K., D.S.; methodology, S.K., D.S., D.R.V.P.M.; software, S.K.; validation, K.S. D.N.K.J., D.G.R.; formal analysis, S.K., D.S., D.R.V.P.M.; investigation, S.K., D.S., D.R.V.P.M.; resources, K.S.; data curation, S.K., K.S.; writing—original draft preparation, S.K.; writing—review and editing, D.S., D.R.V.P.M., K.S., D.N.K.J., D.G.R., A.I.; visualization, D.N.K.J., D.G.R.; supervision, K.S., D.N.K.J., and project administration, K.S., D.N.K.J.; funding acquisition, D.N.K.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded, in part, by the Scheme for Promotion of Academic and Research Collaboration (SPARC), Ministry of Human Resource Development, India under the SPARC/2018-2019/P145/SL, in part, by the framework of Competitiveness Enhancement Program of the National Research Tomsk Polytechnic University, Russia in part, and, in part, by the International cooperation project of Sri Lanka Technological Campus, Sri Lanka and Tomsk Polytechnic University, Russia, No. RRSG/19/5008.

**Acknowledgments:** The authors would like to acknowledge the support granted by Proscans Diagnostics Centre, the leading and reputed Pathology Lab network in Chennai, Tamilnadu, India, for providing real clinical images of the brain MRI.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

**Ethical Approval:** This article follows the ethical standards of 1964 Helsinki declaration with its future amendments.
