You will get Brain Tumor Segmentation Using Swin Uneter
Project details
I will develop an AI-based solution to segment brain tumors from MRI scans using advanced deep learning models. The process will include data preprocessing, model inference, and post-processing to generate accurate segmentation masks of tumor regions. Final outputs may include annotated images, tumor volume calculations, and optional visualizations or reports. This project is ideal for medical research, diagnosis support, or longitudinal tumor analysis.
Machine Learning Tools
Amazon SageMaker, NumPy, OpenCV, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$300
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 10 days | 25 days | 60 days |
Number of Revisions | 2 | 5 | Unlimited |
Number of Model Variations | 1 | 2 | 4 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
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Completed the project successfully.
About Usama
AI Developer | Voice AI & AI Agents | LLM | RAG | AI SaaS Development
91%
Job Success
Taxila, Pakistan - 11:16 am local time
I bring 5+ years of software engineering experience as an AI Developer, delivering elite Artificial Intelligence products and AI SaaS Development architectures using Python, Node.js, Next.js, FastAPI, React, and Supabase.
𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 (AI Development & Architecture)
▸ AI Agent Development & Agentic Systems: As an expert in AI Agent Development, I architect multi-step autonomous AI Agents featuring tool calling, memory, and advanced reasoning. My core AI Development process relies on LangChain, LangGraph, CrewAI, MCP servers, and custom OpenAI/Claude AI model integration pipelines to run complex business workflows.
▸ Voice AI & AI Voice Agents: I built, ultra-low latency Voice AI solutions and inbound/outbound AI Voice Agents using Vapi, Retell AI, Bland, Synthflow, and Twilio. Every AI Voice Agent is optimized with WebSocket streaming for applications like an AI receptionist, an automated AI caller, AI cold calling systems, and real-time lead qualification.
▸ RAG Chatbots & Knowledge Systems: Building production-grade (RAG) architectures and custom AI chatbot semantic systems. I built data extraction pipelines to connect your AI SaaS cleanly to vector databases (Pinecone, Qdrant, pgvector) for highly accurate, hallucination-free AI implementation.
▸ AI Chatbot Development: Building enterprise-ready, context-aware conversational AI chatbots driven by the OpenAI API, Claude, and Gemini. Every custom AI chatbot features structured logging, multi-turn reasoning, and deterministic tool-calling hooks.
▸ AI SaaS Development for Founders: Launching complete AI SaaS platforms and investor-ready AI MVPs in 4–6 weeks. I handle your entire AI application lifecycle from raw database schema design to frontend interface work and automated user onboarding pipelines.
▸ Enterprise AI Automation & API Integration: Designing, programmatic AI automation infrastructure. I write custom Python microservices for CRM automation and deep API integration, securely embedding your custom AI Agents and AI chatbot pipelines directly into HubSpot, Salesforce, Pipedrive, and GoHighLevel.
Note: I focus exclusively on programmatic software engineering, custom Python scripting, and full-stack AI SaaS Development.
𝐑𝐞𝐜𝐞𝐧𝐭 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐀𝐈 𝐁𝐮𝐢𝐥𝐝𝐬
▸ AI SaaS Workspace Platform: A multimodal RAG workspace built with a node-based AI Developer canvas, utilizing OpenAI vector stores, Whisper transcription, and a Qdrant database for comprehensive data indexing.
▸ AI Voice Receptionist SaaS: A multi-tenant, 24/7 inbound/outbound Voice AI calling platform featuring deep Vapi API integration, custom billing, and a multi-tenant React dashboard.
▸ AI Outbound Sales Agent: An ultra-low-latency outbound AI Voice Agent pipeline running on a custom Python, ElevenLabs, Deepgram, and Twilio infrastructure with CRM synchronization.
▸ PropTech AI Automation Platform: An automated document parsing platform combining a Python/FastAPI backend with an AI model integration pipeline for instant contract analysis and RAG verification.
𝐇𝐨𝐰 𝐈 𝐖𝐨𝐫𝐤 (𝐀𝐈 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧)
I write strict evaluation protocols before I ship a line of code. Every AI Agent, enterprise AI chatbot, and streaming Voice AI Agent is systematically stress-tested against real edge cases—not just happy-path scenarios. I utilize Ragas for RAG evaluation, Langfuse for deep execution tracing, and structured logging so that your AI SaaS runs perfectly at scale.
𝐀𝐈 & 𝐋𝐋𝐌 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐭𝐚𝐜𝐤
Artificial Intelligence Foundations: Python, Node.js, JavaScript, TypeScript, Next.js, React, FastAPI
AI Agent Development Frameworks: LangChain, LangGraph, CrewAI, AutoGen, MCP, OpenAI SDK
Voice AI Ecosystem: Vapi, Retell AI, Bland, Synthflow, ElevenLabs, Whisper, Deepgram, Twilio
RAG & Vector Storage: Pinecone, Qdrant, ChromaDB, pgvector, LlamaIndex
AI SaaS Architecture: Supabase, PostgreSQL, Docker, AWS, Stripe, Vercel
𝐖𝐡𝐨 𝐈 𝐖𝐨𝐫𝐤 𝐖𝐢𝐭𝐡
▸ SaaS Founders who need a production-grade AI MVP or complete AI SaaS platform engineered right the first time.
▸ B2B Enterprises looking to scale operations, deploy autonomous AI Agents, and automate complex customer data workflows.
▸ High-Growth Agencies white-labeling custom engineering solutions and AI chatbot systems for enterprise clients.
𝐋𝐞𝐭'𝐬 𝐒𝐡𝐢𝐩 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦
Whether you need to deploy autonomous AI Agents, audit a hallucinating AI chatbot, optimize a complex RAG pipeline, or build a complete, multi-tenant AI SaaS application from scratch, let's map out your project architecture.
Message me with a summary of the business problem you want
Steps for completing your project
After purchasing the project, send requirements so Usama can start the project.
Delivery time starts when Usama receives requirements from you.
Usama works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Preprocessing
Review provided MRI scan formats Normalize intensity values, resample, and align volumes Convert DICOM to NIfTI if required
Model Setup
Load or fine-tune a pre-trained segmentation model. Prepare model environment.