ESP32 Real-Time Oral Cancer Detector -- 2

@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

@hayteekeys

HI, KINDLY READ THROUGH MY PROPOSAL I will develop a highly optimized TinyML model for on-device oral cancer early detection running efficiently on your ESP32 board, delivering real-time image analysis with low power and memory footprint. MY APPROACH ✅ Phase 1: Adapt a lightweight CNN (MobileNetV2 or EfficientNet variant) with aggressive quantization, pruning, and knowledge distillation for ESP32 constraints. ✅ Phase 2: Curate/augment training data and implement on-device preprocessing pipeline for camera input. ✅ Phase 3: Optimize with TensorFlow Lite Micro/ESP-IDF, deploy firmware, and validate performance metrics. RELEVANT PROJECTS - Several TinyML computer vision projects on ESP32 for medical and edge image classification. - Optimized CNN deployments achieving real-time inference on resource-constrained microcontrollers. DELIVERABLES - Trained & quantized model weights - Complete firmware source code - Build/run documentation and validation report QUESTIONS 1. What camera module are you using and any resolution constraints? 2. Do you have an initial dataset of oral images (or need help sourcing/augmenting)? 3. Preferred framework (TensorFlow Lite Micro, ESP-DL, or Arduino)? Ready to start immediately. I can deliver a working proof-of-concept firmware with clinically-oriented accuracy and instant feedback.

@alluankit01

Hi, I can develop your ESP32-based oral cancer detection proof-of-concept with camera input, on-device preprocessing, TinyML inference, optimized firmware, trained model weights, and reproducible documentation. The best solution is to first review your ESP32 board, camera module, available dataset, image resolution, RAM/flash limits, target latency, and expected validation metrics. Then I’ll design a lightweight model using TinyML CNN/MobileNet-style architecture with quantization, pruning, image resizing, normalization, and ESP32-compatible inference through TensorFlow Lite Micro or ESP-IDF. I’m comfortable with ESP32, Arduino/ESP-IDF, embedded AI, TensorFlow Lite Micro, computer vision, image preprocessing, TinyML model compression, data augmentation, edge inference optimization, firmware development, and validation reporting. Deliverables will include: * ESP32 camera capture firmware * On-device image preprocessing * TinyML model architecture * Quantized/pruned trained weights * TFLite Micro integration * RAM/flash optimization * Inference speed testing * Accuracy/sensitivity validation report * Source code and build guide * Run/reproduction documentation I’ll focus on building a realistic low-power proof-of-concept for early screening support, with clear validation results and no unsupported clinical claims beyond the tested dataset. Best regards Ankit

@CyberSasu

With my all-encompassing background in embedded systems and IoT development, I am the perfect candidate for your ESP32 Real-Time Oral Cancer Detector project. I possess extensive practical experience with various low-power boards, including the ESP32, which will enable me to create a feasible, compressed AI architecture that meets your budget but does not compromise on accuracy or speed. My proficiency in AI and machine learning in relation to embedded systems and the ability to build end-to-end solutions make me confident about tackling data collection, augmentation, and on-chip preprocessing aspects of the project effectively. Furthermore, my familiarity with ESP-IDF/Arduino, TensorFlow Lite Micro and other related tools coupled with my expertise in Software Development, Linux Server Administration, and Web Development ensures I can optimize inference time and efficiently manage memory footprint/RAM usage on the ESP32. This would mean significant gains for you as these crucial factors of latency, memory consumption, and computational speed are paramount for edge devices like the ESP32.

@Shrutibimpl

Hello, I will develop the optimized embedded firmware and edge AI model for your ESP32-based oral cancer screening proof-of-concept. I will utilize TensorFlow Lite for Microcontrollers to compile and run a highly compressed convolutional neural network natively on the device. To fit within the ESP32’s limited static memory, I will apply post-training integer quantization to shrink the model size significantly while preserving classification accuracy. I will configure the camera module to capture raw image data, implement lightweight image-cropping and resizing preprocessors in C++, and route the data directly to the quantized model's input tensor for fast, offline inference. Finally, I will optimize the MCU’s power-consumption states and execution loops to ensure the processing cycle operates efficiently on battery power. I have successfully deployed several highly optimized convolutional neural networks and computer-vision pipelines on ESP32-CAM and STM32 microcontrollers using quantized TensorFlow Lite models. 1) Are you using a specific ESP32 board, such as the ESP32-CAM with the OV2640 sensor, and does it include external PSRAM? 2) Do you already have a labeled image dataset of oral lesions to train and quantize the classification model? 3) What is your target inference latency, such as under one second or two seconds, for the diagnostic output? Thanks, Bharat

@hexabitzprojects

Dear Client, Your edge-AI medical prototype closely aligns with my experience in embedded systems, computer vision, and AI deployment on ESP32-based platforms. I have over 15 years of experience developing embedded hardware and firmware, along with AI-enabled vision systems. I have successfully developed OCR-based Number Plate Recognition (ANPR), Face Recognition, Object Detection, and ADAS-level computer vision projects, as well as biomedical and IoT products. My approach would be: Evaluate lightweight TinyML architectures such as MobileNetV2, TinyCNN, or TensorFlow Lite Micro models suitable for ESP32 deployment. Optimize the model using quantization, pruning, and efficient image preprocessing to minimize RAM, Flash usage, and inference latency. Integrate the camera pipeline, preprocessing, and inference into ESP32 firmware with efficient memory management and real-time performance. Validate the solution by measuring accuracy, inference speed, memory footprint, and power consumption. I look forward to discussing your dataset, target ESP32 hardware, and deployment strategy. Best regards, Subhash Suman Embedded Systems | AI | Computer Vision | ESP32 | TinyML | 15+ Years Experience

@Habib821

Hi, Your project is very interesting, and I would like to help. I have experience with ESP32, embedded systems, and AI deployment on resource-limited devices. For this project, I can: * Design or adapt a lightweight TinyML model for the ESP32 * Optimize the model using quantization and pruning to reduce memory and improve speed * Implement image preprocessing and inference using TensorFlow Lite Micro or ESP-IDF/Arduino * Measure inference time, memory usage, and overall performance * Validate the model on a separate test dataset and provide accuracy metrics I will provide the firmware, source code, trained model, documentation, and build instructions so you can reproduce the results on your ESP32 board. I would be happy to discuss your camera module, dataset, and target ESP32 board before we begin. Thank you.

@agoel31102006

Hello, I'm interested in developing your ESP32-based TinyML proof of concept for early-stage oral cancer detection. I'll design a lightweight CNN or MobileNet-based model optimized with INT8 quantization and pruning for fast, low-memory inference using TensorFlow Lite Micro on ESP32. The workflow will include dataset preparation, preprocessing, augmentation, model training, optimization, and deployment. I'll optimize RAM, Flash usage, and inference latency using ESP-IDF or Arduino while keeping power consumption low. The solution will be validated on a held-out test set, reporting accuracy, sensitivity, specificity, precision, recall, F1-score, and false-positive rate to evaluate real-world performance. Deliverables include complete firmware, source code, trained and optimized model weights, build instructions, and concise documentation so the project can be reproduced on your ESP32 board. Estimated timeline: 2–3 weeks. I look forward to discussing your hardware and project requirements.

@manishs188

Proposal: Ultra-Low-Power Edge AI for Oral Cancer Detection (ESP32) I specialize in stripping down heavy AI workloads for constrained hardware. Having placed in the top 6% globally in the Vesuvius Challenge for high-precision computer vision, I know how to deliver a fast, fully offline PoC on the ESP32. 1. Architecture & Compression Standard models (even MobileNet) are too heavy. I will design a custom Micro-CNN (4-5 Depthwise Separable layers) using INT8 Quantization-Aware Training (QAT) via TensorFlow Model Optimization. QAT shrinks the footprint 4x without degrading clinical reliability, aided by magnitude-based weight pruning. 2. Data & On-Chip Preprocessing Using public medical datasets, I will apply heavy augmentations (color jitter, synthetic LED glare). To save SRAM, the ESP32 will capture RGB565 frames, crop the ROI, and downsample to 64x64 using optimized C++ before inference. 3. Toolchain & Optimization I will use native ESP-IDF and TensorFlow Lite Micro (TFLM). Integrating ESP-NN will hardware-accelerate INT8 operations, dropping latency to milliseconds. Weights will be mapped directly from Flash memory. 4. Clinical Validation I will evaluate the model on a strictly held-out set using ROC-AUC and F1-Scores, tuning thresholds to prioritize Sensitivity (Recall) to ensure zero false negatives. Deliverables Python training pipeline, trained weights (TFLite/C-arrays), compiled ESP-IDF firmware, C++ source, and build documentation.

@MohammadkarimKz

Hi, I just finished reading your project, and I think I'd be a great fit for it. I already have a clear idea of how I'd approach the work, and my goal is to deliver a solution that's clean, reliable, and built with attention to detail. I also believe good communication makes every project run more smoothly, so I'll keep you updated throughout the process and make sure we're always on the same page. Before we move forward, I just have a couple of quick question: • Is there anything you'd like me to prioritize? • Do you have any existing files or documentation I should review before getting started? If you'd like, we can have a quick chat to go over the details. Once we're aligned, I'll get started right away. Looking forward to chatting with you!

@itsAhmedShafiq

Hi, I'm a Senior AI/ML Engineer with production experience building deep learning models under tight compute budgets - a shared-backbone multi-head CNN optimized for lightweight inference, plus segmentation/detection systems (SegFormer, YOLO) shipped from notebook to served, latency-tuned production. Honest upfront: my strength is model design, compression, and validation - I'd approach ESP32/TFLite Micro with the same rigor, collaborating closely on firmware rather than overclaiming embedded history I don't have. Approach: Model: MobileNetV3-Small or custom depthwise-separable CNN, int8 quantized (QAT if needed), targeting ~100-250KB to fit ESP32 limits Data: patient/session-level train/val/test splits to avoid leakage, augmentation for lighting/angle variance from handheld capture Device: TFLite Micro + ESP-IDF, profiled layer-by-layer early to correct architecture before deep training Validation: sensitivity, specificity, false-positive rate reported explicitly - not just accuracy Framing: POC output should read "flag for review," not "diagnosed" Deliverables: trained weights, quantized model, ESP32 firmware, preprocessing source, build doc with accuracy/latency/memory trade-off table.