AI ECG Diagnosis Platform

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, I can build a complete AI-powered ECG diagnosis system using Python, TensorFlow/Keras, and Streamlit (or Flask if preferred), delivering a fully reproducible solution from data preprocessing to deployment. The workflow will include image preprocessing, augmentation, model training (leveraging a proven CNN architecture with transfer learning where appropriate for better accuracy and transparency), and inference to classify ECG images as Normal, Arrhythmia, or Myocardial Infarction, along with confidence scores. You'll receive the complete source code, training notebook, documented dataset details (or licensed public dataset links), the trained model, a clean web interface for single or batch image uploads, a detailed README, installation guide, and a project report covering methodology, architecture, evaluation metrics, results, and future work. I'll also remain available after delivery for bug fixes and a viva preparation session to explain the implementation and answer technical questions.

@iimtpradeepdash

I handle web app builds, deployment, integrations, and production ML model ops, but I do not build or train research-grade AI diagnosis models for medical images from scratch. For your request—training, reporting, dataset curation, and academic support—it’s outside my offered stack. If you have a pretrained model or a specific open-source repo (PyTorch, Tensorflow) you want productionized on a web UI, I can wrap, deploy, and document that with a clear interface and support. If you want deployment only, point me to the model checkpoint and architecture, and I’ll package the inference interface with a fast, clean UI. Pradeep

@nexvative

Hi, I can build your complete ECG image classification system with a trained deep-learning model and a clean Flask/Streamlit web interface for predicting Normal, Arrhythmia, or Myocardial Infarction with confidence scores. I can deliver: • Deep-learning model training and fine-tuning using a suitable CNN/transfer-learning architecture • Complete preprocessing, augmentation, class balancing, and hyperparameter tuning workflow • Curated dataset or verified dataset links with licensing details and documentation • Training notebook with clear, commented code for viva preparation • Evaluation using accuracy, precision, recall, F1-score, confusion matrix, and other relevant metrics • Saved model weights and reproducible inference pipeline • Web app supporting single-lead and multi-lead ECG image uploads • Prediction results with confidence probabilities • Linux/Windows installation guide and detailed README • Project report covering methodology, architecture, results, limitations, and future improvements • Post-delivery bug fixes and a brief Q&A session for viva preparation I can adapt a proven open-source backbone where appropriate while clearly documenting every modification and training step. The model will be trained specifically for the provided ECG image data rather than simply wrapping an existing classifier. Estimated delivery: 10–14 calendar days. Total fixed cost: 25000 INR, depending on dataset complexity and final scope.

@rishi9343

As an experienced full-stack developer with a focus on PHP, Python, and Software Architecture, I believe I'm your ideal candidate for this project. With over 8+ years of experience in not just mobile app development but also backend service development and database optimization, building AI-based platforms with ready-to-run systems is second nature to me. In regards to your expectations, my well-commented training notebook details every preprocessing, augmentation, and tuning step. I will also provide you with the complete source code, a concise installation guide, along with a thorough README document that walks you through every line of code. Furthermore, my background in database processing and visualization ensures smooth handling of your data. Your expectation for transparency fits perfectly with the way I work - I can build the model and UI entirely from scratch or adapt an existing open-source framework depending on your preferences. And yes, I'll remain available for quick bug fixes post delivery and even to help you prepare for your viva. To summarize, I am armed with all the requisite skills and expertise needed to create a clean web interface wrapped around an efficient AI model for ECG diagnosis. With my deep comprehension of Flask and Streamlit frameworks for web development, I can assure you high-quality work within the projected timelines. Let's get started on this transformative project today!

@Ritesh11gupta

Creating an AI system for ECG images sounds exciting! I have experience with Python and Flask, which will help in building a smooth platform. What's your vision for the user interface? Would love to hear more!

@alluankit01

Hi, I can build your AI ECG diagnosis platform as a ready-to-run academic/demo system with image upload, model prediction, confidence score, source code, training notebook, README, and project report. The best solution is to use Python with Flask or Streamlit, train/fine-tune a suitable CNN/open-source backbone on a properly documented ECG dataset, and clearly show preprocessing, augmentation, evaluation, and limitations. The app will accept single-lead or multi-lead ECG images and return Normal, Arrhythmia, or Myocardial Infarction with probability scores. I’m comfortable with Python, Flask/Streamlit, deep learning, image preprocessing, model training, evaluation metrics, Linux/Windows setup, and viva-ready documentation. Answers: 1. Cost: ₹3,500 INR. Timeline: 5–7 days. 2. I will transparently adapt/fine-tune an open-source backbone. 3. Yes, I’ll support quick bug fixes and a short Q&A session. Deliverables include model weights, training notebook, web app, dataset/licence notes, install guide, README, report, and bug-fix support. Best regards Ankit

@w3loop

Hi, I can build this as a complete academic AI/ML prototype for ECG image classification with a clean Flask or Streamlit interface. The solution can include: • Single-lead and multi-lead ECG image upload • Classification into Normal, Arrhythmia and Myocardial Infarction • Prediction probabilities/confidence score • Deep-learning model with documented preprocessing, augmentation and tuning • Proper train/validation/test split with class-balance analysis • Transfer-learning backbone where appropriate, with full transparency about the architecture and any open-source components used • Complete training notebook and source code • Dataset details, licensing/source links and cleaning methodology • Web interface loading saved model weights for inference • Installation guide and detailed README • Project report covering methodology, architecture, metrics, results and future improvements • Post-delivery bug fixes and a short Q&A session for viva preparation I’ll keep the implementation reproducible and explain the code clearly so you can confidently present the project during your viva. **Timeline:** 10–14 calendar days **Total Cost:** ₹3,500 INR This will be delivered as an academic/research prototype and not represented as a clinically validated diagnostic system. I can start by finalizing the dataset and model approach before development. Best regards, Techavinya

@dmytrot16

⭐⭐⭐⭐⭐Hello Have you ever felt the urgency of quick, accurate ECG image diagnostics right at your fingertips? Picture the ease of uploading a lead image and receiving an immediate diagnosis—Normal, Arrhythmia, or Myocardial Infarction—with a confidence score. This AI system can provide that precise solution, ensuring timely and reliable results for your medical assessments. I prioritize quality over price. Guaranteed on-time delivery & 100% satisfaction! To achieve this, I propose building a robust deep-learning model tailored to your specific needs. Leveraging Flask or Streamlit for the web interface, the model will be trained on a meticulously curated dataset with a clear class balance and detailed preprocessing steps. The system will include a user-friendly web app that seamlessly integrates the model for accurate predictions and probabilities, all with a concise guide for easy installation and understanding. In a similar project, we developed a medical image classification system that significantly reduced diagnosis time for healthcare professionals. By following a meticulous training process and ensuring model interpretability, our solution brought about a 30% increase in diagnostic accuracy within the first month of deployment. Ready to revolutionize your ECG diagnostics? Shall we dive into the project details and discuss how we can bring this cutting-edge system to life for you? Warm regards

@UmairBuildsAI

Hey, I can help build a complete AI ECG diagnosis platform with a clean web interface that accepts ECG image uploads, runs inference, and returns the predicted diagnosis along with confidence scores. I can also provide well-structured source code, documentation, deployment guidance, and a simple UI that's easy to demonstrate. You can review my work here: https://www.freelancer.pk/u/UmairBuildsAI I specialise in AI-powered web applications, Python, Flask, API integrations, and full-stack development. I can integrate a reliable deep learning model, build the upload and inference pipeline, document the preprocessing and workflow clearly, and deliver a production-ready application with installation instructions and a project report suitable for academic presentation. My approach would be to use a proven deep learning backbone, integrate it into a Flask or Streamlit application, provide a clean inference pipeline, and thoroughly test the complete workflow. I'll also remain available for bug fixes and post-delivery support. Could you please confirm whether you already have a preferred ECG dataset or model in mind, or would you like me to recommend a suitable open-source approach for this project? Happy to jump on a quick call to review the project flow and get your project live smoothly. Best Regards, Umair

@gami00

Hello, I have experience building AI-based automation systems, machine learning solutions, backend APIs, and web dashboards. I understand this project requires a complete end-to-end ECG analysis system, including model development, inference pipeline, and a user-friendly interface. I will develop: • Deep learning model for ECG image classification (Normal, Arrhythmia, Myocardial Infarction) • Complete preprocessing, augmentation, training, and evaluation pipeline • Flask/Streamlit web application for image upload and prediction • Confidence score visualization with results • Clean source code, training notebook, README, and project documentation • Deployment and installation guide for Linux/Windows For transparency, I can use a proven open-source deep learning backbone and fine-tune it with a suitable ECG dataset, while documenting all preprocessing, training decisions, and evaluation metrics. Delivery includes: ✓ Complete source code ✓ Trained model weights ✓ Dataset details/licence information ✓ Report and viva preparation documentation ✓ Post-delivery support for bug fixes and Q&A Estimated timeline: 10-15 days Estimated cost: 15k INR depending on final requirements. I can discuss the architecture, dataset selection, and milestones before starting to ensure the system meets your expectations.

@MdRaihanAli6210

Hello, I can build a complete ECG image AI system with a trained deep-learning model, Streamlit web app, source code, training notebook, documentation, README, and project report. Cost: **$800** (negotiable). Delivery: **12–15 days**. I'll use a proven transfer-learning backbone with full transparency and provide post-delivery bug fixes plus viva support.

@Gurpreetsing343

As a team at GSINFOTECHH OPZ, we absolutely understand the significance of this project - it demands utmost precision and efficiency. Our extensive experience in all-things-linux combined with our proficiency in Python (which we all know is a key part of creating AI-based applications) makes us eminently well-suited for building this AI ECG Diagnosis Platform. Being committed to transparency, I assure you that we will follow your set guidelines scrupulously while developing this platform. We can either create your project from scratch following the trajectory you gave, that will cover everything from training code to the installation guide, or we can blend an existing effective open-source infrastructure into your system. We lean toward using './streamlit' if speed and clarity are imperative as it allows more seamless web integration with Python. A "ready-to-run" platform that meets your quality expectations and specifications within the given timeline is what we aim for. Moreover, as your dependable tech partners, we will always be at your disposal even post-delivery ensuring quick bug fixes and answering any questions you might have even during your viva! Let's collaborate to build something that truly stands out!

@jaybeedkar

Hello, I have reviewed your requirements and can develop a complete ECG image classification system with a clean, easy-to-use web interface using Flask or Streamlit. Cost: ₹3,500 INR (negotiable based on final scope) Delivery: 7 days I will use a reliable open-source deep learning backbone (such as EfficientNet or ResNet) and fine-tune it on an appropriate ECG dataset. This ensures a transparent, accurate, and maintainable solution while clearly documenting every modification. The project will include: Trained deep learning model with saved weights Well-commented training notebook ECG image preprocessing and augmentation pipeline Flask/Streamlit web application for image upload and prediction Confidence score with each diagnosis README with setup instructions and code explanation Project report covering methodology, results, and future scope Dataset details with licensing information After delivery, I'll be available for bug fixes and a short Q&A session to help you understand the implementation and prepare for your viva. I focus on clean code, proper documentation, and timely delivery. I'd be happy to discuss milestones and start immediately.

@abhikc01

With over 3 years of experience in AI, I've successfully developed and deployed numerous end-to-end solutions including AI model development, deep learning, and software architecture. My specialties lie in Computer Vision and Image Processing; areas perfectly aligned with your ECG diagnosis project. In fact, I've previously designed a Retina Vessel Segmentation model that achieved an impressive accuracy of 95% over 320 real retina scans. Utilizing robust frameworks such as PyTorch and TensorFlow/Keras, my approach to your project would include not only building the required deep-learning model, but also providing a well-commented training notebook that details every preprocessing, augmentation, and tuning step taken. I am familiar with Flask and Streamlit, the acceptable web interface options you mentioned, ensuring a smooth transition from model development to its integration into an intuitive and clean web app. In terms of transparency and comprehensive delivery, I ensure the provision of the entire source code along with clear instructions for deployment on Linux/Windows systems. My experience extends beyond just developing the solution, I'm here to facilitate during the entire process from solving bugs to a briefing for your viva defence. Choose me as your trusted partner for a reliable & cost-effective ECG Diagnosis Platform!

@badrez2

Hi there, Classifying ECG images into Normal / Arrhythmia / Myocardial Infarction is a well-trodden path, and the part that decides your viva grade isn't the accuracy number — it's whether you can defend every line under questioning. That's exactly what we build for. You get a clean, reproducible project: a fine-tuned model with saved weights, a fully commented training notebook, a Streamlit app that takes an ECG upload and returns the class plus a confidence score, patient-level train/val/test splits (the leakage trap examiners love to probe), and a report plus line-by-line README written so the work is genuinely yours to explain. Our team ships image→preprocess→structured-output pipelines — most relevantly an OCR system with the same backbone as an image classifier, tested and documented. Happy to share the repo so you can see our code standard. We've already sketched a working POC for this. To your three questions: (1) we can deliver in 5 working days — model + notebook by day 3, app + docs by day 5 — with 3 milestones (model, app, docs/report); (2) transfer learning on a pretrained backbone (best science on a small dataset), everything else written for you, fully transparent in the report; (3) yes — bug fixes plus a viva-prep Q&A session, scoped in writing. Two quick things: on the standard public dataset the classes don't map cleanly (there's a "History of MI" — merge it into MI or keep separate?), and shall we target 12-lead images? A visible "academic use only" disclaimer we'll include by default. Let's talk milestones and start.

@ashish7patel1212

Hello, I can develop a complete AI-powered ECG diagnosis system that classifies uploaded ECG images into Normal, Arrhythmia, or Myocardial Infarction with confidence scores through a clean Streamlit web application. What I'll deliver: * Deep learning model (PyTorch/TensorFlow) with saved weights * Fully commented Jupyter notebook covering preprocessing, augmentation, training, and evaluation * Streamlit web application for ECG image upload and real-time prediction * Dataset details, licensing information, and preprocessing documentation * Complete source code with a well-structured README * Installation guide for Windows and Linux * Project report covering methodology, model architecture, results, and future improvements * Post-delivery bug fixes and support, including a short viva preparation session I will build the complete pipeline while using a proven pre-trained backbone (such as EfficientNet or ResNet) through transfer learning to achieve strong performance efficiently. Every step will be documented clearly, ensuring transparency and making the project easy to understand. Estimated Delivery:10–14 days Budget: Negotiable I have experience working with Python, PyTorch, Computer Vision, Deep Learning, and AI applications, and I focus on writing clean, well-documented, and maintainable code. I look forward to discussing your requirements and getting started. Best regards, Ashish Patel

@shaikhaman7889

I worked on Deep learning models and i can complete your work with perfection please consider me for the same. Thankyou

@Royalitservices

Hi, Thanks for sharing your project. It aligns well with work I’ve successfully completed for other clients, and I’m confident I can deliver a high-quality solution tailored to your requirements. Before providing an accurate timeline, scope, and budget, I’d like to clarify a few key details to ensure we're aligned from the start. Freelancer’s proposal character limit makes it difficult to cover everything thoroughly here. If you're available, let's connect via chat. I can walk you through similar projects I've completed, discuss the best approach for your specific requirements, and answer any questions you may have. Once I understand your expectations, I'll provide a clear execution plan with realistic timelines and pricing. I’m ready to get started as soon as we finalize the details and look forward to working with you. Best regards, Mayank Sahu