SKarthik
I build full-stack web applications and machine learning systems. Experienced in creating responsive web tools using Java, React, Next.js, MongoDB, and SQL, and designing hybrid quantum-classical neural networks (QCNN) for medical image computer vision pipelines.
My Timeline
An interactive walk through my academic background, technical phases, publication landmarks, and current engineering milestones.
Secondary Education
Z P High School
Completed secondary school board examinations, establishing a strong academic foundation. Graduated with a board score of 96.3%.
Intermediate Board (MPC)
Sri Vivekananda Jr College
Studied Mathematics, Physics, and Chemistry (MPC) during pre-university board intermediate education, graduating with a score of 71.3%.
Integrated M.Tech in Software Engineering
VIT-AP University
Enrolled in the 5-year integrated post-graduate software engineering program. Focused on data structures, database designs, OOP, and system algorithms. Maintained a CGPA of 8.58/10.0.
Brain Tumor Detection using QCNN
Quantum Machine Learning Research
Conducted medical computer-vision research. Designed hybrid deep learning systems using pre-trained convolutional features and parameterized quantum circuits (QCNN) in PennyLane/TensorFlow to classify MRI brain scans, publishing two IEEE conference papers.
Hybrid QCNN with ResNet-50
Advanced Deep Learning & Feature Modulation
Designed a high-performance hybrid quantum-classical neural network integrating a Quantum Feature Modulation Unit (QFMU) with a pre-trained ResNet-50 backbone to adjust intermediate feature maps dynamically.
Full-Stack Development & Task Management
Web Application Project Phase
Engineered and deployed a Task Management System. Built a responsive React dashboard, designed custom serverless REST API endpoints in Next.js, and integrated MongoDB Atlas for cloud document persistence.
Technical Skills
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Java
3+ YearsCore OOP, multithreading, Stream API, collections
SQL
2+ YearsStructured queries, join optimizations, indexing
Python (Basic)
1.5 YearsBasic scripting, numpy manipulation, model evaluation
JavaScript (Basic)
1 YearBasic ES6 syntax, asynchronous operations, basic DOM calls
Product Showcase
Detailed engineering archives. Click on any project card to expand it into a complete, deep-dive walkthrough.
Task Management Web Application
Full-stack task planner constructed with Next.js API serverless routes and MongoDB, fully deployed on Vercel.
Brain Tumor Detection using QCNN
Hybrid Quantum-Classical Neural Network leveraging simulated qubits for brain tumor detection in MRI scans.
Hybrid QCNN with ResNet-50
State-of-the-art hybrid QCNN with ResNet-50 backbone achieving ~91% validation accuracy on multi-class brain tumor classification.
Research Publications
Academic research papers published in IEEE. Click an item to expand the abstract, copy index citations, or download PDF manuscripts.
Brain Tumor Detection using Quantum Convolutional Neural Networks (QCNN)
S. Karthik, K. Snehanvitha, Addanki Sai Kumar
Abstract
Accurate and timely detection of brain tumours from MRI images is critical in medical diagnostics. Traditional machine learning and deep learning methods, like classical CNNs, require large datasets and extensive computational resources. To overcome these drawbacks, a hybrid Quantum Convolutional Neural Network (QCNN) approach is developed, combining classical preprocessing with quantum circuits for feature extraction and classification. The system preprocesses, downsamples, and encodes images using PyTorch, then processes features on a simulated 4-qubit quantum circuit with PennyLane, before final classification with classical neural layers. This hybrid QCNN achieves strong classification results and demonstrates improved generalization and efficiency, especially when working with limited data, showing clear benefits over purely classical approaches.
Brain Tumor Classification using Hybrid QCNN with ResNet
S. Karthik, Suma Dasari, Akhila Sree Menda, Chirra Venkata Rami Reddy, Sapthagiri Miriyala
Professional Credentials
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Oracle Cloud Infrastructure 2025 Certified Foundations Associate
Oracle University
Fundamentals of Digital Marketing
Google Digital Garage
HTML & CSS for Web Development
Certification Provider
GitHub Dashboard
Dynamic repository directory and contribution commit log tracked directly from git history metrics.
Karthik7661
github.com/Karthik7661Language Distribution
Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer.
Hybrid Quantum–Classical model for brain tumor classification using Quantum FiLM modulation and ResNet-18. Supports multi-class MRI tumor detection with quantum circuit integration.
Task dashboard website built with a React frontend, custom Next.js serverless REST API endpoints, and MongoDB Atlas database persistence.
Reservation system website built with Next.js, React, and Tailwind CSS.
Get In Touch
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