AI-Based PET Scan Cancer Diagnosis & Treatment Prediction App (Research MVP)

@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

@siddhantsingh326

Hello, This is a clinical-grade ML pipeline, not a standard prediction model, and it needs strong data handling, multimodal learning, and validation discipline. I can design a symptom → risk scoring → imaging → recommendation architecture that is fast, reproducible, and medically structured. Proposed ML Architecture • Tabular + text models for symptoms, vitals, lifestyle & genetics • ICD-10 mapping via clinical NLP (entity extraction + normalization) • Risk stratification layer (triage scoring + confidence bands) • PET-scan analysis using CNN/ViT models (PyTorch / TensorFlow) • Multimodal fusion for cross-validation of clinical + imaging signals Model Targets • Diabetes, Cancer, Heart Disease classifiers • AUROC ≥ 0.90 with calibrated outputs • Evidence-based recommendation engine (clearly separated from clinician input) Deployment • REST / GraphQL API for mobile integration • Dockerised training + inference pipelines • Optimised inference (<3s mid-tier cloud) Tooling Python, PyTorch, TensorFlow, OpenCV, FastAPI, Docker, clinical NLP stacks. Deliverables • Trained & validated models • Symptom assessment interface logic • PET imaging pipeline • API layer • Reproducible training scripts + README Looking forward to collaborating.

@hotanloc

I saw your project description carefully. I can complete this task with high quality on time. I have expertise in ML/DL, and neural network, SVM, decision tree, and other all models.I I have full experiences in TensorFlow, PyTorch, Scikit-learn and other libraries. I I will provide you with the finest work that perfectly aligns with your time and budget constraints. Please send me your message to discuss your project detail more...I am waiting your reply now. Thanks.

@webmasters486

Having reviewed your project requirements, I would like to strongly endorse my skills and experience in AI/ML model development for this research MVP. With dedicated expertise in Python, PyTorch, and MONAI - an essential part of your project architecture - I am thoroughly experienced in working with medical imaging datasets, particularly PET scans, and have completed several similar projects. Moreover, as a committed problem solver, I assure you of my commitment to delivering high-quality work within tight timeframes. I am adept at leveraging 3D CNN and UNet models for deep feature extraction and tumor segmentation and possess a strong command over important evaluation metrics like accuracy, ROC curve analysis, Dice Score, amongst others mentioned in your project description. I propose that we leverage my skills as much as possible in the initial stages to produce effective cancer diagnostics models before gradually advancing into the treatment prediction module. This approach will ensure holistic development supported by clinical data analysis like age, stage, biomarkers alongside image features. By doing so, we aim for a highly accurate diagnosis coupled with well-explained treatment response predictions with Grad-CAM visualizations for absolute transparency

@devanshkhandekar

Hi,I’m a seasoned Applied ML Engineer (6+ yrs)& have built E2E ML products in production & research settings: data pipelines,3D CV models,explainability & APIs with clean, reproducible repos. Relevant experience: • Medical/3D imaging workflows: DICOM ingestion,volumetric preprocessing,3D CNN/UNet training,checkpointing & evaluation (AUC/PR,Dice/IoU) • Explainability: Grad-CAM/attention heatmaps + overlays for review & debugging • Deployment: FastAPI inference,Dockerized builds,reproducible training scripts,model versioning & demo-ready web UIs • Risk/triage prototypes: calibrated probabilities & audit-friendly experiment logs How I’ll deliver your MONAI roadmap Dataset strategy: start with a public PET dataset (TCIA) + a clear split protocol & preprocessing plan (MONAI transforms: spacing/orientation/intensity/augmentations) MVP Phase 1 (Day:1-4): PET cancer objective you choose (binary/multi-class/segmentation). Train MONAI 3D ResNet (or 3D UNet/DynUNet for segmentation), evaluate & export weights +metrics MVP Phase 2 (Day: 5-6): explainability (heatmaps/ROI), FastAPI endpoints (upload -> inference -> JSON) & simple web UI (upload + results + heatmap). Phase 3 (Day: 7-11): treatment-response prototype via deep feature extraction + clinical covariates Deliverables • Working web MVP + FastAPI backend • Well-structured code + Docker + setup guide • Trained model(s) + demo video + documentation/limitations I can commit to 11-12 days & code ownership transfer under NDA

@AhmadXpert2410

Hi there, I can help you design and deploy a high-precision AI pipeline for chronic disease diagnosis covering Diabetes, Heart Disease, and Cancer, combining structured clinical data, symptom-text mapping, and PET-scan image analysis. With experience in healthcare analytics, NLP-to-ICD mapping, and computer-vision models in PyTorch/TensorFlow, I can build reproducible training workflows, risk-scoring models, and fast inference APIs that meet AUROC ≥0.90 targets with strong validation practices. My approach is to create a modular architecture: symptom ingestion + ICD mapping (using medical ontologies), risk-scoring models with lifestyle/genetic inputs, and PET-scan analysis using OpenCV preprocessing and CNN-based models. Outputs will be clearly separated into AI insights vs clinician notes, containerized with Docker, and exposed through REST/GraphQL APIs for your mobile team, with secure handling of sensitive clinical data and detailed documentation for maintainability. You’ll receive tested models, reproducible scripts, deployment-ready containers, and a short handover guide. I’m happy to review your datasets, compliance needs, and performance targets to propose milestones and ensure a reliable, scalable diagnostic engine for your app. Regards, Ahmad

@chiragardeshna

Hi there, I am a strong fit for this MVP because I have built PyTorch-based computer vision systems with structured training pipelines, model evaluation, and API deployment for research-grade applications. I have worked with 3D medical image workflows using MONAI-style preprocessing, DICOM handling, CNN-based classification and segmentation models, and feature extraction pipelines that extend into downstream prediction tasks. I would implement a 3D classification-first approach using MONAI with TCIA PET datasets, structured preprocessing transforms, checkpointed training with ROC-AUC monitoring, Grad-CAM explainability, and a FastAPI backend serving inference to a lightweight React or HTML interface. I reduce risk by keeping scope disciplined to diagnosis first, using reproducible data pipelines, versioned model checkpoints, containerized deployment, and clear documentation of metrics and limitations to avoid unrealistic clinical claims. Regards Chirag

@amaank66

Hello, We specialize in building clinical-grade AI systems for healthcare, combining structured data modeling, medical NLP, and medical imaging into unified diagnostic platforms. Your vision for a multi-layer diagnostic pipeline (symptoms → risk scoring → imaging validation → recommendations) is architecturally sound and aligned with real-world clinical AI systems. Our Approach Disease models: Train/fine-tune models for Diabetes, Heart Disease, and Cancer with high precision/recall Symptom intelligence: NLP mapping of free-text inputs to ICD-10 terminology Risk scoring engine: Lifestyle + genetic + vitals triage layer Imaging AI: PET-scan interpretation using PyTorch / TensorFlow + OpenCV Decision layer: Evidence-based recommendation engine with strict separation of AI output and clinician notes API layer: REST/GraphQL service for mobile/web integration Engineering Standards Containerised ML pipelines (Docker) Reproducible training workflows AUROC-driven model evaluation Optimised inference (<3s target) Secure data handling Full API documentation We build production-grade medical AI, not demos — modular pipelines, validated models, and scalable deployment architecture. Once we review your data sources and clinical datasets, we’ll define milestones, validation strategy, and delivery phases. If you’re serious about building a clinically reliable AI diagnostic platform, we’re ready to engage immediately. Best regards, Amaan Khan P. CUBEMOONS PVT LTD.

@rakam26

Hello Just read your post and it seems you are looking for a skilled AI specialist experienced in clinical data modeling, multimodal diagnosis systems, and medical image analysis to build a production-ready chronic disease diagnostic platform. With my years of extensive experience and exceptional expertise in developing high-performance ML models for structured clinical data, ICD-10 mapping, PET-scan image analysis using PyTorch/TensorFlow and OpenCV, multimodal risk-scoring systems, and containerized REST API deployments with reproducible training pipelines, I am 100% confident that I can bring your vision to life in the shortest possible time. Let's connect and see how great value I can add to your business. Best Regards, Raka

@shadabkhan92

As a seasoned full-stack developer with deep expertise in AI/ML and healthcare applications, I'm excited to bring my skills into creating your AI-based PET Scan Cancer Diagnosis & Treatment Prediction App. Leveraging on Python, PyTorch and MONAI libraries mentioned in your job description has become second nature to me. My experience handling huge datasets from repositories like TCIA, conducting model selection and data preprocessing will be invaluable for your project. multi-class classifications as needed. Using 3D UNet or Attention UNet for tumor segmentation will ensure the app precisely locates the tumor regions. I have excellent exposure to fast adoption algorithms using which I can train the app to prognosticate whether patients will survive treatment, recur or be responsive. My previous projects have included feature extraction using ML models, opting for a fully connected neural network or an XGBoost classifier depending on the needs. To ensure transparency that is a priority in healthcare, explainability mode will be added through Grad-CAM, Attention maps and Heatmap overlay on PET image. This will highlight not only the tumor but also bring out attributes that led to inference generation. Building web interfaces is my area of specialization; you can be sure that the PET scan upload process, diagnosis display and precision scoring heatmap representation will be seamless on your web interface.

@InjectAI

I fully understand your requirements and can develop a research-level AI/ML prototype for PET scan cancer diagnosis and treatment prediction. I have extensive experience in medical imaging AI, MONAI, PyTorch, and building web-based ML research tools. .................. What I will Deliver .................. Data preprocessing pipeline using MONAI for DICOM PET scans 3D CNN / UNet-based diagnosis and optional tumor segmentation models Feature extraction and treatment prediction module combining imaging + clinical data Explainable AI outputs (Grad-CAM / heatmaps) for model transparency FastAPI backend with endpoints for scan upload, inference, and result retrieval Lightweight web interface to upload scans and display diagnosis, confidence, heatmaps, and treatment prediction Trained model files with clear instructions for loading and inference Well-structured, modular, and documented source code Demo video showing functionality Deployment-ready instructions for local or Docker-based research use .................. Tech Stack for this Project .................. Python 3.9+, PyTorch, MONAI, NumPy, scikit-learn pydicom for DICOM handling FastAPI for backend API React / HTML / JS for front-end Optional: Docker for deployment, GPU support for training and inference I offer introductory rates and guarantee a maintainable, research-ready MVP. You can view my portfolio and similar AI/ML projects on my GitHub and LinkedIn profiles. Regards, Malik Abdul Salam AI / ML Engineer

@WiredAI

Dear Client, This aligns perfectly with our expertise. At WiredAI Ventures, we design end-to-end AI diagnostic systems—from symptom ingestion to imaging validation. We can train high-AUROC models for Diabetes, Heart Disease, and Cancer, map inputs to ICD-10, integrate PET-scan analysis (PyTorch/TensorFlow), and deliver sub-3s inference via a containerised REST/GraphQL API. Happy to discuss milestones and timelines. Best regards, WiredAI Ventures

@saranyakuduvan

Hai, I have carefully read all your requirement. My suggestion is 1. First we have finalize the dataset ( Main thing is that we need the groundtruth dataset for segmentation purpose) 2. Preprocessing : will use the appropriate processing technology. If it has an unbalanced dataset or a small dataset, we can do data augmentation(based on dataset). 2. Image segmentation : I will use 3D U-Net or some other pretrained model for segmentation. if it didn't works means we have to do transfer learning for our dataset. SO regarding this step we will discuss . 3. Feature extraction / Classification : So we can use 3D ResNet or 3D DenseNet or EfficientNet for feature extraction or Classification . ( Based on the performance, we can conclude that if we perform feature extraction algorithms, we can use an advanced ensemble machine learning classifier for classification. ) 4. Treatment prediction goals: Based on the exact probability, we can decide that. But if you switch to a journal publication or public review, the reviewer may ask how you decide this without experts, so we need to consider this matter. 5. Deployment : possible deployment method will use as per your requirement. I have more than 5+ year experience in medical image processing. Basically I am a professional MATLAB and python developer and a journal article writer for PhD. In offline mode I am handling more than 10+ schools. Thank you very much, I am looking for war for your message.

@devpratiksha

Hello! As per your project post, you’re looking to build an AI-powered chronic disease diagnosis app focused on Diabetes, Heart Disease, and Cancer, with fast, reliable inference and actionable recommendations. My approach will be to design and deploy high-precision AI/ML models using TensorFlow or PyTorch, fine-tuned to achieve AUROC ≥ 0.90 for each disease. The symptom-assessment module will map structured or free-text inputs to ICD-10 codes, and a risk-scoring layer will weigh lifestyle and genetic data to triage patients. PET-scan imagery analysis will be integrated seamlessly using OpenCV or deep learning pipelines to cross-validate findings. Personalized lifestyle and treatment suggestions will be generated following evidence-based guidelines, clearly distinguishing AI recommendations from clinician notes. All components will be packaged behind a REST or GraphQL API, containerized in Docker for reproducible training and deployment, with inference under 3 seconds on a mid-range cloud instance. I have 7+ years of experience in AI and healthcare applications, including predictive modeling, image analysis, and building scalable APIs for mobile and web integration. You will receive fully documented source code, containerized training pipelines, and test-ready models for seamless integration. I’m confident I can deliver a robust, accurate, and production-ready AI diagnostic system that meets your performance and reliability requirements. Best regards, Pratiksha Gupta

@shazimjaved

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Dataset Collection & Preparation ⟶⟶ MONAI Preprocessing & Augmentation ⟶⟶ Deep Learning Model Development (3D CNN / UNet) ⟶⟶ Feature Extraction & Treatment Prediction ⟶⟶ FastAPI Backend Development ⟶⟶ Web Interface Implementation ⟶⟶ Testing, Validation & Documentation ⟶⟶ Final Delivery Key Highlights ✔ Research-level AI/ML development — PET scan analysis using MONAI and PyTorch. ✔ Diagnosis & treatment prediction — 3D CNN or UNet for tumor detection, feature extraction for treatment response. ✔ Explainable AI — Grad-CAM / attention maps to highlight tumor regions and model focus. ✔ FastAPI backend — upload, inference, and result endpoints with JSON response. ✔ Web interface — simple, responsive frontend displaying diagnosis, probability, heatmap, and treatment prediction. ✔ Dataset strategy — public PET datasets (TCIA) with proper preprocessing, augmentation, and normalization. ✔ Deployment-ready MVP — local or cloud deployment with documented setup instructions. Sign-off Best Regards, Shazim AI/ML Developer | Medical Imaging Specialist | MONAI & PyTorch Expert

@BrainstackTech

Hello there, We have around 8 years of rich experience in AI/ML model development, medical data pipelines, and production API design. The part of your brief that stands out is the PET-scan cross-validation against text-based findings — getting that multimodal fusion right is what separates a demo from a real diagnostic tool. For the symptom-assessment module, we'd build a structured pipeline: free-text inputs get normalized through a medical NER layer (scispaCy + a fine-tuned BioBERT model) that maps to ICD-10 codes before anything hits the diagnostic models. The risk-scoring layer would use chain-of-thought prompting with structured JSON output — lifestyle and genetic inputs scored independently, then fused with clinical signals. For PET-scan analysis, we'd go with PyTorch over TensorFlow here because torchvision's pretrained medical imaging backbones (especially ResNet-based architectures fine-tuned on RadImageNet) give us a faster path to your AUROC ≥ 0.90 target than training from scratch. Output validation is critical in medical AI — every model response passes through a confidence-gated filter before surfacing recommendations. On cost optimization, we'd use smaller specialized models for the symptom-to-ICD-10 mapping (fast, cheap, high accuracy on a narrow task) and reserve larger models only for the recommendation engine where nuanced reasoning matters. Redis-backed caching on repeated symptom patterns keeps inference costs low and helps hit your 3-second end-to-end target on a mid-range cloud instance. Hallucination control is non-negotiable for clinical output. Every AI suggestion gets tagged with a confidence score and evidence source, clearly separated from clinician notes as you specified. We'd implement retry logic with a fallback to a simpler ensemble model if the primary model returns low-confidence results, plus strict rate limiting on the REST API layer. We've built AI-powered analytics dashboards for ESG and compliance reporting — similar pattern of ingesting mixed-format data, running it through classification models, and generating actionable recommendations with audit trails. That structured extraction pipeline maps directly to your symptom-assessment-to-diagnosis flow. Happy to share a prompt architecture diagram showing the processing pipeline. We'd break this into three milestones over 45 days: M1 (Days 1–15) — disease classification models trained, validated, Dockerized. M2 (Days 16–30) — symptom assessment, risk scoring, PET-scan integration. M3 (Days 31–45) — recommendation engine, API packaging, documentation. Weekly video updates plus async Slack throughout. Looking forward to hearing from you. Naveen Brainstack Technologies

@Hemantt841

I understand that you’re developing an AI app focused on diagnosing chronic diseases like Diabetes, Heart Disease, and Cancer, with a workflow that includes a symptom-assessment module, risk scoring, and PET-s

@ayushmaanksahu4

Hi there, You’re absolutely in the RIGHT PLACE. I’ve delivered SIMILAR PROJECTS multiple times and know EXACTLY how to execute this efficiently and correctly from day one. To lock down the SCOPE, TIMELINE, AND PRICING, I’ll need to ask you a few key questions. Unfortunately, Freelancer’s 1500 CHARACTER LIMIT doesn’t allow me to break everything down properly here. Let’s jump on CHAT so I can show you my PROVEN PAST WORK, walk you through the REAL RESULTS I’ve delivered, and outline a CLEAR ACTION PLAN for your project. You’ll immediately see why my approach is DIFFERENT and EFFECTIVE. If you’re serious about getting this done RIGHT, I’m ready to move forward. Looking forward to CONNECTING and WINNING TOGETHER. Cheers, Aayushaman Sahu

@WCTech

Hello, I’m an AI/ML Healthcare Solutions Specialist with 7+ years of experience building clinical prediction models, medical imaging pipelines, and scalable AI APIs. I specialize in combining structured clinical data, NLP, and computer vision to deliver fast, reliable diagnostic support systems. Your Requirement You need an AI-powered system that assesses symptoms, risk factors, and PET-scan data to flag Diabetes, Heart Disease, and Cancer with high accuracy, generate evidence-based recommendations, and expose everything through a fast, production-ready API for mobile integration. What I Will Deliver End-to-end ML pipeline to train/fine-tune models for Diabetes, Heart Disease, and Cancer (AUROC ≥ 0.90 target) Symptom assessment module mapping free-text/structured inputs to ICD-10 terminology using NLP Risk scoring engine combining lifestyle, vitals, and genetic indicators PET-scan image analysis using TensorFlow/PyTorch + OpenCV for cross-validation Real-time inference pipeline (<3s) optimized for mid-range cloud deployment Evidence-based recommendation engine clearly separating AI insights from clinician notes REST/GraphQL API for seamless mobile/front-end integration I can assure you 100 % satisfied job. If you have some questions, you can contact me so we can discuss in detail. Let's have a QUICK CHAT on the details to proceed further with this project. Thank’s, neha~~ AI/ML Healthcare Solutions Specialist