Advanced Brain Disorder Detection AI

@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

@pkundu25

I am an experienced AI Developer. Your job caught my eye and looks to be quite interesting to me as I developed Fetal brain abnormality detection for multiclass image classifications in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am confident that I will be able to help you by developing AI powered advanced brain disorder detection system using transfer learning with a view to achieve higher accuracy. Similar work done in the past: - Fetal brain abnormality detection - Real-time face recognition system - Multiclass intent classification - AI Powered Copilot for Text2SQL Query - Semantic search engine - Topic modeling Relevant Skills: - Python - Django/Flask - Transfer Learning/SOTA Architectures - GPT4o/Gemini/Llama3.2/Mistral - Numpy, pandas, scikit learn, matplotlib - LangChain - MySQL - TensorFlow/Kras - Google Colab - OpenCV Let's have a chat to understand the project objective and the dataset in details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@innovateNsolve

Hello, I am a researcher and trainer in computer vision, machine and deep learning having PhD in Computer Science with 25+ years of experience. I have worked on several research projects in these domains with few research publications as well. I have strong theoretical knowledge with hands-on experience in AI/ML, Computer Vision, python, OpenCV, Tensorflow, MATLAB etc. I have strong experience in Computer Vision, Deep Learning, CNN architectures, Transfer Learning, and AI-based image analysis systems. Your project involving multi-class MRI classification for brain tumors and Alzheimer’s disease is technically feasible and aligns well with my expertise. I would be happy to discuss: Dataset quality, Deployment expectations, Accuracy goals, Future scalability Looking forward to collaborating with you. Thank you.

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe) & I can help build this MRI-based multi-class brain disorder detection system with a clean,reproducible deep learning workflow & deployable demo My approach: >>First,I’ll review & clean the combined Kaggle MRI datasets,remove duplicates/leakage,standardize class labels,& create proper train/validation/test splits >>Build preprocessing steps for resizing,normalization,augmentation,class balancing,& image-quality checks >>Train baseline CNN,then improve using transfer learning with VGG16,ResNet50/EfficientNet,& compare results using accuracy,precision,recall,F1-score,ROC/AUC where applicable,& confusion matrix >>Apply hyperparameter tuning for learning rate,batch size,epochs,dropout,dense layers,& augmentation strategy >>Add Grad-CAM/explainability heatmaps so predictions are interpretable & visually show MRI regions influencing the model >>Package the best model into a Streamlit/Hugging Face app where users can upload an MRI image & receive predicted class,confidence score,& explanation heatmap >>Deliver clean notebooks/scripts,trained model,evaluation report,README,& deployment support Relevant past work:I have experience in medical computer vision & healthcare AI,including dental X-ray grading,elderly cardiac-risk prediction using clinical data,lab anomaly detection on MIMIC-derived healthcare records,explainable ML pipelines for diagnosis support,& production-grade image classification/object-detection systems

@nexvative

Hi, I am an experienced Computer Vision Engineer and have worked before with MRI images. Your job caught my eye and it is exactly the types of projects I work on a day to day basis. Here are a couple examples of the similar work I have done before - Brain Tumour Segmentation - Data Augmentation and Classification of different types of Alzheimer's. Let's have a chat to understand the project objective and the dataset in details. I assure you that I will deliver the best quality results and ensure your fully satisfied. Looking forward to hearing from you soon. Thank you

@sudhir0112

Hi! I'm Sudhir Jain — MIT graduate, ML Engineer specializing in Computer Vision and medical AI. I have built advanced neural network systems for brain disorder detection using MRI/CT imaging data. For your Advanced Brain Disorder Detection AI project, I'll deliver: multi-class brain disorder classification (tumor, Alzheimer's, epilepsy, etc.), 3D volumetric MRI analysis with CNN/ResNet architectures, transfer learning from medical imaging pre-trained models (Med3D, BrainSuite), data augmentation for limited medical datasets, HIPAA-compliant data handling, and clinical validation metrics (sensitivity, specificity, AUC). Python, PyTorch/TensorFlow, NiBabel for neuroimaging. Rigorous testing and documentation. Let's build this lifesaving AI!

@manoval09

Hi, you need automated disease detection from MRI scans — that's a computer vision classification problem where accuracy directly impacts clinical decisions. I'll build this using transfer learning with a pretrained ResNet or EfficientNet backbone on TensorFlow, adding a custom classification head for your disease categories. This approach reuses existing medical image features rather than training from scratch, which accelerates training and improves accuracy on limited datasets. I'll handle MRI preprocessing (normalization, contrast adjustment, augmentation) and implement train/validation/test splits with stratification to prevent class imbalance from skewing results. First 24 hours: I need three clarifications — do you have the MRI dataset ready, what specific brain diseases are you targeting, and what validation metric drives success (sensitivity, specificity, AUC-ROC)? Once I have those answers, I'll propose a phased architecture and realistic timeline, because scope this complex deserves honest scoping upfront. Best regards, Val

@indreshpnk

Hello, I understand you need an AI-based brain disorder detection system using MRI scan images with deep learning (CNN + transfer learning) to classify Glioma, Pituitary, Meningioma, Alzheimer’s, and Healthy brains with high accuracy and a deployable prediction system. Here’s what I can provide: • CNN model development with VGG16/ResNet50 transfer learning • MRI image preprocessing, normalization, and augmentation • Multi-class classification with Softmax output • Training, tuning, and evaluation (accuracy, precision, recall, F1-score, confusion matrix) • Deployment using Streamlit or Hugging Face for real-time prediction I bring 4+ years of experience in Machine Learning, Deep Learning, and computer vision projects using TensorFlow, Keras, and OpenCV, focusing on robust and production-ready AI systems. Just to clarify: • Do you already have a finalized dataset or should it be curated/cleaned further? • Preferred deployment platform: Streamlit web app or API-based system? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@umairinayat1

As an experienced ML/AI Engineer who specializes in Computer Vision and full-cycle ML development, I'm uniquely qualified to tackle your advanced brain disorder detection project. My skills and expertise span not only CNN and Transfer Learning techniques like VGG16 and ResNet50, but also extend beyond to include the With a strong academic background and several years of experience in the field delivering production-grade AI/ML systems, I fully understand the end-to-end lifecycle of such projects - from preprocessing image data for training to deploying robust models for efficient diagnosis. This is demonstrated through my vast portfolio which includes numerous advanced CV systems such as YOLOv8-based shoplifting detection system, DCPose-based sports pose tracker and AR face filters solution with MediaPipe landmarks. My work has always focused on delivering high levels of accally, I have valuable experience working on projects with potential healthcare implementations - similar to your vision of providing fast automated diagnosis to minimize manual effort for doctors while supporting early treatment planning. By selecting me for this job, you'd be partnering with an experienced professional who can deliver a thoroughly comprehensive implementation with unparalleled accuracy that is above 90%, using performance metrics like Accuracy, Precision, Recall, F1-score combined with state-of-the-art techniques like Confusion Matrix analysis. I look forward to discuss

@shubhams0520

Hi, I reviewed your project requirements carefully. You need a machine learning solution capable of accurate prediction and clear visualization. I have hands-on experience with Python, Scikit-learn, EDA, Streamlit, and model optimization techniques like Random Forest, XGBoost, and cross-validation. I’ve also built and deployed real-world ML projects focused on prediction systems. My approach will include data preprocessing, feature engineering, model training, accuracy optimization, and deployment of an interactive application. I can start immediately and deliver clean, scalable, and well-documented work within the deadline. Let’s discuss the project details further.

@Ahmadw208

Hi, Thank you for sharing the project details. Before proceeding, I would like to clarify a few points: * Will the dataset and existing codebase be provided? * Do you already have trained models, or should training be done from scratch? * Is the requirement focused on research/FYP-level implementation or production-level deployment? * What deployment platform do you prefer for the final system? * What is the expected timeline for completion? Once I have these details, I can better estimate the workflow and availability. Thank you!

@gerog8

Hi, I am Geronimo Garello Koch, an AI Engineering student at the Universidad Nacional del Litoral (UNL). I am highly interested in contributing to your MRI-based brain disorder detection system using my engineering background and technical focus on medical AI applications. Proposed Technical Solution: Advanced Model Architecture: I will implement Transfer Learning using VGG16 and ResNet50 to perform multi-class classification for Glioma, Pituitary, and Meningioma tumors, as well as Alzheimer’s disease. Precision Preprocessing: Using OpenCV and NumPy, I will handle image normalization (0-255 to 0-1) and feature extraction to ensure the CNN models capture critical diagnostic patterns. Clinical Reliability Metrics: I will go beyond accuracy to prioritize Recall, F1-score, and Confusion Matrices, ensuring the system minimizes false negatives for safer medical diagnosis. Automated Deployment: I will build the user interface using Streamlit and host the model on Hugging Face, creating a fast and non-invasive diagnosis tool for healthcare professionals. My engineering training provides the mathematical rigor and research-oriented mindset necessary to handle complex datasets and hyperparameter tuning (learning rate, batch size, etc.). I am a fast learner, dedicated to the ethical implementation of clinical AI, and ready to start immediately. Best regards.

@amirullahamzah

Hello! I'm Amirullah, a Computer Vision and Deep Learning Engineer with hands-on experience building perception pipelines at MIMOS (Malaysia's National R&D Centre) and UTP. I've worked extensively with ResNet-based architectures, semantic segmentation, and custom CNN models, which align well with your MRI brain disease classification project. I'd love to help build a multi-class system covering Glioma, Pituitary, Meningioma, Alzheimer's, and healthy scans using CNN, VGG16, and ResNet50 with Transfer Learning. I can handle preprocessing, hyperparameter tuning, evaluation metrics, and deployment via Streamlit or Hugging Face. Tech stack: Python, TensorFlow, Keras, OpenCV. Happy to discuss further!

@rahmac51

Hi there, Reading your project description felt like reading the documentation of a medical AI project I recently built. As a Computer Engineer, I have already developed a highly accurate Brain Tumor Detection pipeline that mirrors almost exactly what you are looking for. I am ready to implement this multi-class classification system for you. Here is how my practical experience aligns with your exact checklist: • Architecture: I have hands-on experience building and fine-tuning CNNs, specifically leveraging VGG16 and ResNet architectures for MRI scan analysis. • Preprocessing: I am proficient with OpenCV and NumPy for rigorous MRI preprocessing, including the exact 0-255 to 0-1 pixel normalization you mentioned. • Training & Metrics: I know how to handle TensorFlow/Keras to tune hyperparameters (batch size, learning rate) to push accuracy above 90%, backed by detailed confusion matrices and F1-scores. • Deployment: I prioritize practical application over just theoretical code. I can quickly package the final model into a clean Streamlit interface or Hugging Face space for fast, automated diagnosis. Since I already have the foundational architecture and experience from my previous medical imaging project, I can deliver a high-performing, deployable system rapidly. Let's connect so I can show you my previous work and we can get started on your dataset! Best regards, Rahma

@jayeshs93

Hello, I have experience working with Deep Learning, CNN models, and MRI image classification projects using Python, TensorFlow, and Keras. I can develop an automated brain disorder detection system capable of identifying diseases such as Glioma, Pituitary Tumor, Meningioma, Alzheimer’s disease, and healthy brain conditions from MRI scans. I will perform image preprocessing, model training, hyperparameter tuning, and evaluation using metrics like accuracy, precision, recall, F1-score, and confusion matrix. I can also deploy the model using Streamlit or Hugging Face for easy prediction through image upload. I am dedicated to delivering accurate, clean, and well-documented work within the given timeline. Thank you.

@emlaaima123

I am a perfect fit for this project thanks to my background in Biomedical Engineering and my practical experience in medical AI. I previously developed a system for "Multi-eye Brain Tumor Classification on MRI Images" using CNNs, achieving an impressive F1 score of 98.1%. Furthermore, leading a technology project supporting Alzheimer's patients has provided me with clinical architecture ready to handle the classification tasks required by this project. With my strengths in the Python ecosystem, TensorFlow/Keras, and advanced hyperparameter tuning skills, I am confident in my ability to optimize the Transfer Learning (VGG16/ResNet50) architectures efficiently and develop smooth model deployments, resulting in a fast and accurate automated system.

@rameshs089

Dear Client, I am the best candidate for your Brain Disorder Detection project. Here's why: I have strong Python, TensorFlow, and Keras skills with hands-on experience in CNN, VGG16, and ResNet50 for medical imaging. I have worked on MRI brain tumor and Alzheimer's detection. I understand preprocessing, normalization, and data augmentation. I will combine Kaggle datasets properly and split 1000 training / 250 testing images as you need. I will tune learning rate, batch size, epochs, and filter size to achieve above 90% accuracy. I will provide Accuracy, Precision, Recall, F1-score, and Confusion Matrix for evaluation. I will deploy your model on Hugging Face using Streamlit. Doctors can upload MRI images and get instant predictions. I use Google Colab, OpenCV, NumPy, Pandas, Matplotlib, and Scikit-learn regularly. I am available 40 hours/week at ₹1000/hour. I can start immediately. My code will be clean, documented, and scalable for adding more diseases in future. I guarantee a fast, automated, non-invasive diagnosis system that reduces manual doctor work. Please award me this project. Thank you, Ramesh Shrestha

@gerard934

With vast knowledge and extensive experience as a software engineer, I believe I am the ideal candidate to deliver exceptional results for your Advanced Brain Disorder Detection AI project. My expertise in utilizing Python, the TensorFlow library, Keras, as well as OpenCV, NumPy, Pandas, Matplotlib, and Scikit-learn aligns perfectly with the technologies needed for your project. Having worked with Google Colab and Anaconda previously will also expedite familiarity and ease-of-use during project execution. I'm confident in my ability to implement Convolutional Neural Networks (CNN) specifically VGG16 and ResNet50 models- vital for analyzing MRI brain scan imagery for successful detection of multiple brain diseases such as Glioma tumor, Pituitary tumor, Meningioma tumor, Alzheimer's disease among others outlined in your project. I am also skilled in transfers learning techniques which we'll use to improve the model's accuracy by employing existing pre-trained models on different datasets.

@selvakumar3314

I am expert in deep learning. I can develop a complete system which detects brain diseases with high accuracy.