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Deep-learning diagnostics
An AI medical assistant using deep learning (TensorFlow) to analyze medical images, generate diagnostic PDF reports, and answer queries via a chatbot.
Long wait times for diagnoses, human error in medical image interpretation, specialist shortages in underserved areas, and inconsistent diagnostic quality all slow down access to care. DiagnoSight AI provides instant analysis of X-rays, MRIs, and CT scans — cross-referencing images with reported symptoms to give evidence-based support for a first clinical opinion, and routing users to the right specialist when they need professional care.
DiagnoSight AI speeds up image interpretation by providing instant CNN analysis of medical scans, cross-referencing findings with patient symptoms to output a first clinical opinion and specialist suggestions.
Processes X-rays, MRIs, and CT scans in seconds using a deep learning CNN model built with TensorFlow — returning a confidence-scored prediction across trained condition categories.
Identifies conditions across 15+ medical categories, providing a quick first opinion on likely diagnosis with probability breakdown and plain-language explanation of findings.
An in-app conversational chatbot that answers users' follow-up questions about their diagnosis results in plain language — reducing anxiety and helping users understand next steps.
Generates a downloadable, formatted PDF report summarizing the diagnosis, confidence scores, image metadata, and recommended actions — for the user to keep or share with a doctor.
Based on the diagnosed condition, recommends relevant specialist types and helps users locate nearby doctors — ensuring users know exactly where to go for professional care.
Complete user authentication, profile editing, password management, and a full saved report history — so users can revisit, compare, or share previous diagnostic sessions.
AI Core
A Convolutional Neural Network trained on labeled medical imaging datasets to classify conditions across 15+ categories. The model is loaded once at startup and performs inference on preprocessed image tensors — returning a probability distribution across all trained classes.
Analysis Pipeline
Accepts JPEG/PNG uploads of X-rays, MRIs, and CT scans. Applies normalization, resizing, and tensor conversion before passing to the CNN. Combines image predictions with symptom inputs to generate a cross-referenced diagnostic result with confidence scores.
Report & Chat Layer
The report generator formats diagnostic results, metadata, and recommended actions into a structured PDF. The chatbot module uses NLP to interpret follow-up questions in natural language and return contextual responses about the diagnosis — keeping users informed without overwhelming them with technical output.
User Layer
Full authentication — registration, login, profile editing, password change. Each user has a persistent diagnostic history stored in the database, with the ability to view, re-download, or delete past reports. Doctor recommendations are surfaced based on diagnosed condition type and location input.
The full source — TensorFlow model, inference pipeline, chatbot, PDF generator, auth system, and UI — is available on GitHub.