Menu | D.Vamsidhar

Courses

This section has the certifications I got for doing different courses and different events.

AWS Cloud Operations

This is the badge received for completing AWS Graduate - Academy Cloud Operations course.

MATLAB Onramp

As student exploring the field of data science, MATLAB is an important tool to learn and use in future.

5 star Python badge

This is the badge received from Hackerrank for python programming.

Data Manipulation with Pandas

A course about using Pandas library for data preprocessing.

Introduction to Tableau

A course on data visualization tool Tableau.

GenerativeAI Meet-up

HACKATHONS

Certifications I got by participating and winning in hackathons.

DATA SCIENCE HACK'22

Participated in the data science hackathon organized in my college.

iDex - Defense Hackathon

Participated in Defense India Hackathon 2022.

HERE INDIA HACKATHON 2025

Winners of HERE INDIA HACKATHON 2025 conducted by HERE Technologies.

PUBLICATIONS

This section contains the papers I published.

Optimized Graph-Based Segmentation for Brain Tumor Detection

Brain tumor segmentation is challenging in medical imaging because misclassifications or wrong segmentations may cause grave treatment errors. This work takes MRI brain images through a normalization process to a standard scale and then performs segmentation by unsupervised machine learning algorithms. This involves two phases: The preliminary Phase; here, the data for MRI is preprocessed through various methods like cropping and contrast enhancement; the Segmentation Phase- evaluation of multiple algorithms includingk-means clustering, Fuzzy C-Means, t-SNE, expectation-maximization and graph-based segmentation (Improvised Felzenszwalb's Technique, IFT) In this, graph-based segmentation had outperformed the others with exact segmentation around the tumor region depending upon the intensity and proximity. The graph-based segmentation algorithm constructs a graph using intensity and spatial features. Regions are segmented through iterative merging based on edge weights and internal differences. This method achieved exact tumor segmentation, visualized using encrusted color maps to delineate tumor regions. Results demonstrate that the graph-based segmentation technique is computationally efficient, lightweight, and outperforms other approaches, which makes it a promising step towards enhanced tumor segmentation in brain MRI analysis. However, some edge cases where the segmentation failed highlight areas for improvement. This research provides a pathway for an efficient pipeline

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