X-ray Image Type Classifier

@AwaisChaudhry

Hello, I’m Muhammad Awais. I will build a robust X-ray image type classifier that focuses on tagging the image type (chest PA, chest lateral, cervical spine, hand, etc.) without diagnosing pathology. I’ll start with a clean, reproducible setup: a Python project using PyTorch or TensorFlow based on what yields the best accuracy and GPU speed. The workflow will include a Dockerized or clearly documented environment, a curated data loader that safely handles DICOM/PNG, and a training script with strong validation to meet the 95% top-1 target. I’ll provide a short README with setup, training commands, and single-file or batch inference, plus a trained model file and a simple CLI. I will ensure no patient metadata is written to disk. The result will be a self-contained, reproducible solution you can run on your side. Draft a concise clarifying question to confirm dataset size, target classes, and allowed inference latency for deployment. Best regards,

@LiveExperts

Hello, As an experienced data scientist and deep learning practitioner with Live Experts, I believe I'm uniquely positioned to deliver the accurate and efficient X-ray image classifier you're seeking. My strong background in Machine Learning (ML), Data Science, and software architecture make me a prime candidate for this project. Additionally, my proficiency in Linux ensures it will run smoothly on your preferred framework (PyTorch or TensorFlow/Keras) and be thoroughly documented for reproducibility. In line with your acceptance criteria, I have a proven track record of exceeding expectations with top-notch accuracy. My deep understanding of handling medical imaging data and knowledge of frameworks such as Pydicom enables me to work seamlessly across different datasets and file formats. Furthermore, my fluency in Docker along with my command over the mentioned tools would ensure that patient-identifiable metadata remains protected during the entire process. At Live Experts, we're committed to delivering clean, understandable codes that are easy to replicate - something I intend to bring to this project. Let's get started today so we can work harmoniously to transform your X-ray image classification needs into a reality! Thanks!

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Deep Learning Model for X-Ray Image Classification ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a deep learning model to classify X-ray images. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in medical imaging. I'll create an efficient model using Python and either PyTorch or TensorFlow/Keras, ensuring high accuracy and fast inference. ➡️ Why Me? I have 5 years of experience in deep learning and image classification, focusing on medical imaging. My skills include data preprocessing, model training, and performance evaluation. Additionally, I have a strong grip on Docker and reproducible environments, which are crucial for your project. ➡️ Let's have a quick chat to discuss your project in detail. I can provide samples of my previous work to showcase my expertise. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Deep Learning ✅ Image Classification ✅ Python Programming ✅ PyTorch ✅ TensorFlow/Keras ✅ Data Preprocessing ✅ Model Training ✅ Performance Evaluation ✅ Docker ✅ Jupyter Notebooks ✅ DICOM Handling ✅ Medical Imaging Waiting for your response! Best Regards, Zohaib

@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

@Herculesz

Hi I’m a full-stack deep-learning engineer experienced with PyTorch, TensorFlow, MONAI, and DICOM pipelines, and I can build a high-accuracy X-ray study classifier tailored to your predefined categories. A key technical challenge is handling mixed PNG/DICOM formats while preserving metadata privacy, and I will solve this through controlled loading pipelines, secure preprocessing, and standardized augmentation. I specialize in medical-imaging models using pydicom and MONAI, ensuring the training workflow is reproducible, Docker-ready, and optimized for GPU acceleration. I can deliver a clean notebook or script, a well-structured inference module, and a trained model achieving strong top-1 performance on your held-out set. The solution will include a robust dataset loader, class-balanced training, and fast batch inference. I can also provide clarity on environment setup and ensure no PHI is ever logged or written. My monthly salary expectation is aligned with delivering expert-level results without being expensive. Thanks, Hercules

@nikhilgarg96

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Nikhil

@hireageek

Hi there, I’m excited about the opportunity to work on your X-ray Image Type Classifier project. With my extensive experience in developing deep learning models for medical imaging using Python and frameworks like PyTorch and TensorFlow/Keras, I am confident that I can deliver a solution that meets your high standards. My previous projects involved similar tasks, where I achieved over 95% accuracy on validation sets, so I understand the nuances of classifying imaging data without engaging in diagnostic pathology. I’ll ensure a meticulous approach to the project, starting with the labeling and organizing of your dataset. I will implement clean, reproducible code in a Jupyter notebook, as well as provide detailed documentation for environment setup and inference commands. The final deliverables will include a trained model file and a simple CLI for easy interaction. Let's set up a timeline that aligns with your readiness to transfer the dataset and address any domain-specific questions you might have. What specific categories do you want to classify within the X-ray images? Thanks,

@Feriver

Hello, I understand that you are seeking a deep-learning specialist to classify X-ray images by type with high accuracy. My approach will involve designing and training a robust model in Python using PyTorch or TensorFlow/Keras, leveraging efficient preprocessing for PNG and DICOM files while ensuring patient privacy. I will deliver clean, reproducible code in a Jupyter notebook or script, a trained model file, and a simple inference function/CLI for single or batch predictions. The environment will be fully containerized with Docker or clearly documented for reproducibility. I have prior experience in medical imaging, including DICOM handling and frameworks like MONAI and fastai for healthcare applications. My goal is to produce a model achieving ≥95% top-1 accuracy while maintaining a secure, reproducible workflow suitable for immediate deployment and testing. Thanks, Asif

@devendrathakur12

Hello, I specialize in medical image AI and have built & customized large scale X-ray and scan classification systems. The main challenge here is getting very high accuracy while keeping data private and inference fast. I am certified in PyTorch and TensorFlow development, and I will solve this by using proven CNN backbones, clean DICOM handling, and GPU-optimized inference. I’ll deliver a trained model, simple CLI, and Docker setup so you can run it anywhere with the same results. This will save setup time and avoid data risks. A few questions to align: Are classes evenly distributed? Do images come from multiple machines? Should DICOM tags be stripped at load time? Is batch speed or single image speed more important? I can start as soon as you share access. Best regards, Dev S.

@dvcontact

Hello, Are you interested in a deep-learning solution that can accurately classify your X-ray images with high reliability? I offer specialized expertise in medical imaging, ensuring top-1 accuracy of 95% or higher, all while maintaining strict confidentiality of patient data. Let's discuss how I can help turn your dataset into a powerful classification model. Best, Smith

@enervell

Hi, I appreciate the detailed requirements for your X-ray Image Type Classifier project and I am confident that I can deliver a robust deep-learning model that meets your criteria. With over 5 years of experience in machine learning and deep learning, particularly in medical imaging, I have successfully developed similar classification models that achieved high accuracy. 1. **Experience with Medical Imaging**: I have previously worked on projects involving DICOM files and utilized libraries like pydicom and MONAI to handle medical images efficiently. 2. **Frameworks**: I am well-versed in both TensorFlow/Keras and PyTorch, and can choose the best framework depending on the expected accuracy and inference speed. 3. **Project Structure**: I will maintain a clean and reproducible codebase using Jupyter notebooks, including a README with setup instructions and inference commands. 4. **Docker Implementation**: To ensure reproducibility, I will encapsulate the entire environment within Docker, adhering to your privacy requirements regarding patient data. I’m eager to start as soon as we finalize the details.

@jinufeb14

With over a decade of experience in Linux-based software development and machine learning, I believe I am the ideal candidate for your X-ray image type classifier project. My technical expertise lies in Python and ML, along with an in-depth understanding of software architecture, all of which are essential for your project’s success. Additionally, my rank in the top 0.03% of freelancers exemplifies my dedication to delivering high-quality results that meet and exceed clients' expectations. While working extensively with cloud technology, I have gained proficiency in creating clean and reproducible code, as you require for this project. I aim to utilize PyTorch or TensorFlow/Keras framework based on the best suited for your task, ensuring both high accuracy and fast inference on modern GPUs. Moreover, my experience with DICOM handling and familiarity with pydicom, MONAI, fastai medical add-ons further demonstrate my capability to navigate the complexities specific to medical imaging projects.

@nrajuu07

As a seasoned data scientist specializing in the realm of biomedical data, I can confidently say that your project aligns perfectly with my expertise. I have worked extensively with medical datasets and various imaging modalities including X-rays, histopathology images, and CT scans. This affords me an intrinsic understanding of the challenges and nuances involved in dealing with DICOM files, handling patient metadata, and ensuring complete adherence to privacy protocols.

@efanntyo

Hi there, ★ ★ ★ Innovative, robust X-ray image type classifier ready to deliver a production-grade solution. I will build a Python-based deep-learning pipeline (PyTorch or TensorFlow/Keras) that accurately classifies X-ray studies into predefined categories (chest PA, chest lateral, cervical spine, hand, etc.). My approach ensures strict adherence to medical imaging best practices (DICOM handling with pydicom, MONAI where beneficial) and excellent reproducibility via Dockerized environments or a fully documented virtual setup. With your dataset, I will implement with focus on: - A clean, scalable folder structure and reproducible training scripts plus a concise README. - A train/val/test split, heavy data augmentation, and class-balanced training to reach top-1 accuracy ≥ 95% on held-out data. - Privacy-by-design: no patient-identifiable metadata written to disk or logs. - A trained model file, and a simple CLI for inference that returns class and confidence. - A Docker-based workflow (or documented virtualenv) for easy reproduction on your side. I bring extensive experience in data science and ML deployment, including building medical-imaging pipelines with DICOM handling, secure logging, and GPU-accelerated training. I will align the project with your requirements, deliver modular, well-documented code, and provide clear instructions to run inference for single images or batches. What is the distribution of categories across your held-out validation set (e.g., how many

@quan9824

As a Full-Stack developer with a knack for AI systems, I understand and have developed solutions using Python, PyTorch and TensorFlow to get the best out of AI models. Your project on X-ray image classification is an exciting challenge I'm well-suited for as I possess extensive experience in machine learning, deep learning specifically for image processing and recognition. Coming to performance, my work is characterized by complete dedication and a strong sense of responsibility which has given me a 100% job completion rate. Ensuring your satisfaction, meeting deadlines and producing outcomes adhering to your acceptance criteria will be my highest priority. My high attention-to-detail ensures clean, reproducible code that you can easily execute, maintaining a high-level of accuracy throughout. Moreover, if given the opportunity, I will make sure no identifiable metadata will be written or logged and will maintain full compliance with the necessary protocols to ensure data security as a topmost priority. Overall, hiring me would not only mean working with a skilled professional but also a warm collaborative partner who deeply values your concerns and tailored services to fit them. Let's leverage our skills together to deliver impactful solutions for your project.

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@denisSolution

Hi client, I'm Denis Redzepovic, an experienced developer with expertise in Deep Learning, Software Architecture, Linux, Keras, Machine Learning (ML), Python, Docker and Data Science. I have worked extensively on diverse Python projects, ranging from backend development and automation to data processing and API integrations. My deep understanding of Python’s libraries and frameworks allows me to build efficient, scalable, and maintainable solutions. I pay close attention to code quality and performance to ensure your project runs flawlessly. With my solid experience, I’m confident I can deliver results that exceed your expectations. I focus on writing clean, maintainable, and scalable code because I know the difference between 99% and 100%. If you hire me, I’ll do my best until you’re completely satisfied with the result. Let’s discuss your project details so I can tailor the perfect Python solution for you. Thanks, Denis

@sf264

Hello! I am interested in developing a robust deep learning model that will look at the collection of X ray images that you have and instantly tell you which predefined category it belongs. I have a vast experience in medical imaging. I delivered a similar project to my client last month and he gave me a 5 star rating. Want to see that project? Just leave me a message. Based on my proven track record of delivering high quality results consistently and a deep understanding of your project requirements, I am very confident in replicating my past success in your project. Looking forward to working with you, Fahad.

@DigitaSyndicate

Reliability isn't a feature; it's a foundational requirement. I’m here to ensure it’s built-in from day one. Your requirement for a high-accuracy X-ray classification system demands an architecture that balances computational efficiency with robust generalisation. Leveraging modern convolutional neural networks within either PyTorch or TensorFlow frameworks, we can optimise for rapid inference on contemporary GPUs while ensuring model interpretability and reproducibility. Addressing DICOM specifics, our approach integrates metadata scrubbing and standardised pre-processing pipelines to maintain privacy and meet regulatory standards without sacrificing performance. The model will scale seamlessly with your dataset growth, maintaining precise classification across diverse anatomical categories. At DigitaSyndicate, a UK-based agency, we don’t just build; we architect for scale. We engineer precision-built automation and AI-driven systems designed for performance, eliminating the need for costly re-engineering phases. Our recent deployment of a medical imaging classifier achieved 97 percent accuracy on a comparable dataset. Could you describe your current data augmentation and preprocessing pipeline to better align with your workflow? Casper M. DigitaSyndicate