Hi, I'm Shubham Gajjar 👋
AI Engineer & Research Specialist pioneering the future with intelligent solutions and cutting-edge artificial intelligence.
About Me
AI Engineer and Researcher passionate about medical AI applications and cutting-edge deep learning solutions
My Journey
I specialize in medical AI applications, particularly brain tumor segmentation and skin cancer classification. My research focuses on developing hybrid neural network architectures that achieve state-of-the-art performance in medical image analysis.
With expertise in PyTorch, TensorFlow, and computer vision, I've achieved 96.3% accuracy in skin cancer classification using hybrid ResNet-ViT models. My work spans from medical AI to game AI, demonstrating versatility across different AI applications.
I'm passionate about advancing AI technology to solve real-world healthcare problems and contribute to systems that improve medical diagnosis and patient outcomes.
Technical Skills
Specialized expertise in AI/ML, computer vision, and research methodologies
Research Publications
Published research in medical AI and computer vision, contributing to healthcare advancement
A Hybrid ResNet-ViT Architecture for Skin Cancer Classification
Shubham Gajjar, Harshal Joshi, Om Rathod, Vishal Barot, Deep Joshi
4th IEEE World Conference on Applied Intelligence and Computing (AIC 2025)
2025 • DOI: Pending - IEEE Xplore
Proposed a hybrid ResNet50-ViT model for skin cancer classification achieving 96.3% testing accuracy. The architecture fuses ViT's global context awareness with ResNet's strong local feature extraction, demonstrating ROC values of nearly 1.00 for all test set classes. This breakthrough enables accurate classification of seven lesion classes from the HAM10000 dataset.
Research Areas
VGG16-MCA UNet: A Hybrid Deep Learning Approach for Enhanced Brain Tumor Segmentation in FLAIR MRI
Shubham Gajjar, Deep Joshi, Avi Poptani, Vishal Barot
International Journal on Machine Learning
2024 • DOI: Pending
Proposed a novel VGG16-MCA UNet hybrid architecture for enhanced brain tumor segmentation in FLAIR MRI images. The approach combines VGG16's robust feature extraction with Multi-Scale Context Aggregation (MCA) and UNet's precise localization capabilities. This innovative architecture demonstrates superior performance in handling complex tumor boundaries and varying contrast levels in FLAIR MRI sequences, contributing to automated brain tumor detection and diagnosis. Paper will be available for download after publication.
Research Areas
Projects
Cutting-edge research in medical AI and innovative AI/ML projects showcasing deep learning expertise
TrackMania Reinforcement Learning Agent
Developed an advanced reinforcement learning agent for TrackMania racing game using Implicit Quantile Networks (IQN). The agent learns optimal racing strategies through trial and error, achieving competitive lap times and demonstrating robust decision-making in unpredictable racing situations.
Snake Game Implementation
Classic Snake game implementation using Python and Pygame. Features smooth gameplay mechanics, score tracking, and collision detection. Demonstrates fundamental game development concepts and object-oriented programming.
Flappy Bird Game Clone
Flappy Bird game clone built with Python and Pygame. Includes physics simulation, sprite management, and user input handling. Showcases game development fundamentals and real-time rendering techniques.
Twitter Sentiment Analysis System
Built a comprehensive sentiment analysis system using Twitter API to analyze public sentiment on various topics. Implements NLP techniques and machine learning models for real-time sentiment classification.
Let's Collaborate
Ready to work on cutting-edge AI research and innovative machine learning projects
Research Focus
Medical AI, Computer Vision, Deep Learning
Expertise
IEEE Publications, Neural Networks, Reinforcement Learning
Research & Collaboration Areas
Medical AI Research
Brain tumor segmentation, skin cancer classification, medical imaging
Deep Learning & Neural Networks
CNN, ResNet, Vision Transformers, hybrid architectures
Reinforcement Learning
Game AI, autonomous agents, evolutionary algorithms
Data Science & Analytics
Sentiment analysis, predictive modeling, statistical analysis
Research & Project Collaboration
Interested in collaborating on cutting-edge AI research or innovative machine learning projects? Let's explore opportunities to advance medical AI and computer vision together.
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