Medical image abnormality detection & auto-report generation
Deep-learning vision system that flags abnormalities in radiology scans and outputs structured findings with heatmap verification.
Topic ideas, runnable source code, VTU-format report, PPT and viva prep, guided step by step by mentors. Send a request and hear back in about 30 minutes.
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Deep-learning vision system that flags abnormalities in radiology scans and outputs structured findings with heatmap verification.
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Low-power wireless controller reading soil moisture, temperature and NPK sensors to drive pump valves and send alerts.
Share branch, semester and topic through the form or WhatsApp.
A mentor checks feasibility and confirms scope and timeline.
Code, diagrams and your VTU-format report are prepared and explained.
Live run on your laptop plus examiner Q&A practice.
A clean repository with setup notes and execution scripts, so it runs the first time on your laptop.
30+ pages in editable Word and PDF.
Google Meet / AnyDesk session.
Slides for Phase 1 and 2 reviews.
DFDs, sequence diagrams, flowcharts.
Likely examiner questions with clear technical answers, so you walk into the viva prepared.
Yes. We support 5th and 6th semester mini projects and 7th and 8th semester major projects across CSE, ISE, AI/ML, ECE and EEE.
Yes. A mentor sets it up and runs it with you over Google Meet or AnyDesk so you can demonstrate it confidently.
Yes. Reports follow VTU margins, fonts and front-matter, delivered as editable Word and PDF.
Usually within 30 minutes during working hours on WhatsApp, and within one business day by form.
Share your topic and deadline. Our mentors will review your problem statement and guide you through code and documentation.
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