Object Detection Model for Disease

@shadabkhan92

Leveraging powerful Machine Learning (ML) methods, I, MOHD SADAB, offer expert solutions in your search for a skilled professional. My extensive experience in building object detection models, PyTorch and Transformers proficiency, and deep knowledge of computer vision align perfectly with the objectives of your project.

@glanegonsalves

Hi, Glane here. I have hands-on experience with deep learning and computer vision, including work on lung CT/medical-image segmentation and brain tumor classification, which gives me a strong understanding of medical imaging workflows, preprocessing, annotation handling, model evaluation, and clinical-image challenges. I can develop the object-detection pipeline using PyTorch and Transformer-based architectures, work with suitable publicly available datasets, and incorporate Explainable AI (XAI) methods to make predictions interpretable through visual explanations and feature/region attribution. I’ll focus not only on model performance but also on robust validation, error analysis, interpretability, and reproducibility, so the final system can clearly demonstrate why and where the model detects disease-related findings.

@muneeb6767

I can develop a PyTorch/Transformers-based object detection pipeline using suitable public datasets, with XAI methods to interpret and visualize model predictions. I’ll focus on clean training, evaluation, explainability, and reproducible code so the results are easy to validate and extend.

@nexvative

Hi, I can develop and evaluate a deep-learning object detection pipeline for disease-related visual analysis using appropriate publicly available datasets, with a strong focus on both detection performance and explainability. My approach would include: • Dataset selection, preparation and validation based on the target disease/use case • PyTorch-based object detection using a suitable modern architecture • Transformers where they provide a measurable advantage • Training, validation and hyperparameter optimisation • Evaluation using mAP, precision, recall, F1 and class-level error analysis • XAI methods such as Grad-CAM, attention visualisation or related techniques to show why the model identifies specific regions • Visualisation of predictions, false positives and false negatives for meaningful model interpretation • Reproducible training/inference scripts with configuration and documentation I’ll keep the pipeline modular so additional datasets, classes or model architectures can be incorporated later. I’ll also clearly document dataset sources, preprocessing, training decisions and limitations, which is especially important for disease-related computer vision. Deliverables will include the trained model, complete PyTorch source code, evaluation results, XAI visualisations and a concise README explaining how to reproduce and extend the work. I’m ready to review the specific disease/use case and recommend the most suitable dataset and detection architecture.

@robertb297

Hi, hope you’re doing well! I’d be very interested in working on this project. I have hands-on experience with PyTorch, deep learning, computer vision, and model evaluation, including CNN-based architectures and Transformer models. I can build and optimize an object detection pipeline using an appropriate public disease-related dataset, then apply XAI techniques such as Grad-CAM or other explainability methods to show why the model makes its predictions. I’ll focus not only on detection performance but also on producing interpretable results that are easy to evaluate and present. I can start immediately and provide clean, reproducible code and documentation. Looking forward to working with you!

@iterativeTech

My name is Prem, an AI and Cloud Data Engineering Specialist. With a strong background in building ML models and a deep understanding of object detection and computer vision, I believe I am the ideal candidate for developing your disease-detection model. I have extensive experience using Python, including PyTorch and Transformers - the very tools you're seeking expertise in. (char 549) When it comes to object detection models, a crucial aspect is interpreting the model's predictions; this is where my skill in Explainable AI (XAI) comes into play. Not only can I build an accurate and efficient model, but I can also provide meaningful explanations about how the model reaches its conclusions. Being able to understand and explain these decisions facilitates trust in the system built. Previous projects involving healthcare data have given me a deep appreciation for the importance of this sector and pushed me towards developing responsible AI systems. With my meticulous approach to designing intelligent solutions aligned with business objectives, you can expect a disease-detection model that is not only robust and efficient but also actively delivers tangible ROI for your organization. Let's collaborate to make a real difference in healthcare through technology. Looking forward to hearing from you soon!

@shayankhan333

For medical detection, DETR or Deformable DETR usually beats YOLO when lesions are small and overlap, and it pairs cleanly with attention rollout for XAI. I'll fine-tune a transformer detector in PyTorch on your chosen dataset, then wire Grad-CAM and attention maps so each prediction is visually explained. One catch: class imbalance in disease datasets skews mAP, so a weighted loss matters more than architecture. 1) Which dataset, chest X-ray, skin lesion, or plant disease? 2) Do you need a training notebook or a deployable inference script? Cheers Shayan

@InjectAI

Hello, I’m an AI/Computer Vision developer with hands-on experience building object detection and deep learning solutions using Python, PyTorch, and modern computer vision techniques. I can develop the disease-focused object detection model using suitable public datasets, including dataset preparation, annotation/format conversion, augmentation, model training, evaluation, and optimization. I can also integrate **XAI techniques** such as Grad-CAM/attention visualization to make predictions interpretable and clearly show which image regions influenced the model’s decisions. My experience includes: * Object detection and image classification * PyTorch and deep learning workflows * YOLO-based computer vision models * Dataset preparation and augmentation * Model evaluation and performance analysis * Explainable AI and prediction visualization * Python, OpenCV, NumPy, and related ML tools I can provide clean, reproducible code, trained model weights, evaluation results, visualizations, and documentation explaining the complete workflow. I’m ready to discuss the target disease, preferred dataset, and model architecture and start immediately. Best regards, Abdul Salam

@RyanF172

We have over 5 years experience with similar projects in computer vision and deep learning. You're looking to develop an object detection model for disease using publicly available datasets, while also requiring expertise in PyTorch, Transformers, and Explainable AI. I would approach this project by first analyzing the relevant datasets to ensure they align with your goals. I will then design a robust object detection model, leveraging PyTorch and Transformer architectures. This will include implementing methods for explainability to help interpret model predictions clearly, addressing your need for transparency. Deliverables: - Object detection model implemented in PyTorch - Detailed documentation of the model architecture - XAI techniques for model explainability - Performance evaluation metrics - Support for model interpretation I am happy to share relevant examples of my previous work. Let’s discuss your specific requirements further. Regards, Ryan

@zobs1

This proposal outlines an end-to-end Machine Learning pipeline to convert raw tabular data into a production-ready classification model. Using Python, scikit-learn, XGBoost, and LightGBM, the workflow focuses on data cleaning, feature engineering, and hyperparameter optimization to maximize precision, recall, F1-score, and ROC-AUC. Key deliverables include clean Python source code, detailed evaluation reports with confusion matrices, an interpretable Google Colab notebook explaining feature importance (SHAP/LIME), and a functional FastAPI REST endpoint for real-time model predictions.

@krishnaedit

Hi, I’m very interested in this project because it closely matches my background in Deep Learning, Computer Vision, and medical AI research. I have hands-on experience developing image classification, segmentation, and detection models using PyTorch, TensorFlow, YOLO, Transformers, and CNN architectures. My relevant experience includes: ✓ YOLOv5-based medical image detection/classification research ✓ Medical imaging projects involving MRI, brain disorders, and disease classification ✓ Computer vision research on gastrointestinal abnormality detection ✓ Object detection, segmentation, and image classification pipelines ✓ Transformer-based and CNN-based deep learning models ✓ Explainable AI techniques such as Grad-CAM and attention visualization for interpreting predictions ✓ Dataset preprocessing, augmentation, imbalance handling, training, evaluation, and model optimization For this project, I can help identify an appropriate publicly available disease dataset, prepare and analyze annotations, build a strong baseline detection model, experiment with modern architectures, and provide explainability to understand why and where the model detects a disease-related region. My approach would include: Dataset research and validation Data preprocessing and augmentation Object detection model development in PyTorch Training and hyperparameter optimization Evaluation using mAP, precision, recall, and F1-score Explainable AI and prediction visualization

@syedalihasnat929

Hi, This project immediately caught my attention because it combines computer vision, deep learning, PyTorch, object detection, and Explainable AI—the exact areas I work with. I can build the disease-focused object detection pipeline using an appropriate public dataset and modern deep-learning architecture, with particular attention to both detection performance and interpretability. I can also provide the supporting training/evaluation code and clear visual explanations of the model’s predictions so the final result is useful beyond simply reporting an accuracy score. I’m comfortable working with PyTorch, Transformers, computer vision, XAI and Python, and I can adapt the implementation depending on the disease, available dataset, and required detection task. If you already have a specific disease or dataset in mind, send it over and I can assess the best direction before we begin. I’m available to start immediately. Best, Syed Ali Hasnat

@mernabaher

Hi, I'm an AI/Computer Vision engineer specializing in deep learning-based object detection and explainability. For this project, I'd build the pipeline as follows: Data: Source and prepare a public medical imaging dataset relevant to your target disease (e.g. NIH ChestX-ray14, ISIC, or Kvasir depending on scope), converting annotations to COCO/YOLO format with proper train/val/test splits. Model: Implement detection using PyTorch with a Transformer-based architecture (DETR or RT-DETR), which fits your stated requirement for Transformers over standard CNN detectors. Explainability: Integrate attention-rollout or Grad-CAM visualizations so predictions are interpretable, showing which image regions drove each detection, not just a black-box output. Deliverables: Trained model weights, an inference script, XAI visualization notebook, and a short report documenting performance (mAP, precision/recall) and interpretability results. I work primarily in Python/PyTorch on Colab and can share intermediate checkpoints for your review throughout. To scope accurately, I'd want to confirm which specific disease/dataset you have in mind before finalizing the timeline, since that affects both training time and dataset licensing. Happy to discuss further, looking forward to working with you.

@rahulk0744

As an experienced full-stack and app developer, I'm excited to bring my diverse set of skills to the table for your object detection project. While my tech stack may not directly align with your listed requirements, Python prominently figures in my repertoire and will be pivotal for us in this project. I have a proven track record of building scalable web applications and high-performance mobile apps, and I'm confident that my experience can effectively contribute to the architectural and backend aspects of your deep learning model. My familiarity with REST APIs, third-party integrations, and databases like MySQL and MongoDB will only aid in building a reliable infrastructure. Additionally, my work spans over 5 years, which means I've seen various trends in technology rise and fall. This makes me adaptable and resourceful as I can swiftly learn new techniques and tools as demanded by the project - such as PyTorch and Transformers. Furthermore, having worked with multiple clients on developing dashboards, landing pages, web applications, API integrations, cross-platform mobile apps, database-driven systems, automation tools, geo-fencing, and wallet/points solutions – I bring a well-rounded approach to problem-solving.

@adeelahmed073

Hi, I can develop the disease detection model using PyTorch and Transformers, with proper dataset preparation, training, evaluation, and XAI methods to explain model predictions clearly.

@ronakkataria1

The gap in this brief is the dataset, not the model. Most public disease datasets are classification sets, not detection sets. PlantVillage is one label per image with no boxes. NIH ChestX-ray14 has 112,000 images and only about 1,000 carry bounding boxes. You can't train a detector on image-level labels. So before any architecture talk: which dataset, and does it ship boxes? If it doesn't, either we annotate a subset or the task becomes weakly supervised localisation, which is a different model and a different accuracy story. Second, XAI on a detector is not XAI on a classifier. Grad-CAM and SHAP assume one classification head. YOLO and DETR predict across multiple scales and queries, so plain Grad-CAM gives you one blurry map per image instead of a reason per box. D-RISE handles this properly because it perturbs the input and scores each detection separately. It's slow, so I'd run it on a fixed held-out subset rather than every image. What I'd do: DETR or YOLO depending on how many boxes you actually have, mAP at 50 and at 50-95 reported per class, D-RISE maps on the same fixed examples every run so you can compare across experiments, plus the training notebook so you can rerun it yourself. Which disease, and which dataset do you have in mind? That decides everything else. Ronak

@marisaparyasto

Hello, I’m interested in your disease-focused object detection project. I have experience with **Python, PyTorch, deep learning, computer vision, YOLO/object detection, and machine learning**, as well as academic experience in engineering and ML. I can help with the complete workflow: • Identify and prepare a suitable public dataset • Build and train an object detection model using PyTorch • Evaluate an appropriate Transformer-based architecture where suitable • Measure performance using precision, recall, F1 and mAP • Implement appropriate Explainable AI (XAI) techniques • Generate detection and explanation visualizations • Analyze model errors and document the methodology/results • Provide clean, reproducible code and setup instructions I would first confirm the specific disease/application and dataset before selecting the final architecture, since these directly affect the appropriate detection and XAI methods. **Bid: ₹12,500 INR** for a clearly defined single-dataset/single-application implementation. If the project requires extensive experimentation, multiple datasets/models, or a full research paper, we can define those as additional scope. I’m available to start promptly and can provide a structured, reproducible implementation. Best regards, Marisa Paryasto

@CauaAlves

I will build a reproducible PyTorch object-detection pipeline around an appropriate public disease dataset, beginning with dataset suitability and annotation-quality checks. The workflow will cover preprocessing, augmentation, train-validation-test separation, a strong baseline detector, documented training, and evaluation using mAP, precision, recall, and per-class error analysis. I will add an explainability component such as Grad-CAM or attention visualization where technically appropriate, plus an inference notebook, saved weights, sample predictions, and clear run instructions.

@stephenb062

Developing an object detection model for disease is a fascinating challenge, especially when leveraging publicly available datasets. I've worked on similar projects that required deep learning expertise, and I understand the nuances involved in achieving precise outcomes. Your emphasis on using PyTorch and Transformers aligns perfectly with my background. I have extensive experience in building object detection models and am well-versed in the principles of Explainable AI, which is crucial for interpreting model predictions effectively. Our focus is on delivering work that's reliable, well thought out and aligned with what the client is trying to achieve, not simply completing another project. I prioritize clear communication and thoughtful approaches tailored to your specific needs. If you'd like to discuss ideas or explore the best datasets for your model, I'm here to chat. It would be great to share insights and guidance that could benefit your project. Regards, Stephen