This repository is dedicated to the AI course on biomedical datasets, conducted under the supervision of Dr. Mohammad Bagher Khodabakhshi, with collaboration from Amir Hossein Fouladi and Alireza Javadi.
YouTube Channel: Course YouTube Channel
GitHub Profiles:
In this project, we focus on implementing and teaching machine learning and deep learning projects in the biomedical field. For each topic in artificial intelligence, we will also address a practical project utilizing diverse and distinct medical datasets, including medical signals and images.
| Section | Link | Description |
|---|---|---|
| Lectures | Link | Includes files from the course lecture sessions. |
| Python Basics | Link | Provides a comprehensive introduction to Python, focusing on essential skills for effective project development. |
| Pandas, Matplotlib, Numpy & Scikit-learn | Link | Covers the four main Python libraries in machine learning, along with a project on regression using tabular medical data. |
| Ensemble Classifier - ECG Arrhythmia Classification | Link | ECG Arrhythmia classification in ECG signals using ensemble learning with SVM and Random Forest models. |
| Ensemble Classifier - Alzheimer Detection | Link | Alzheimer’s Disease (AD) detection using MRI features extracted from the ADNI dataset using ensemble learning methods (e.g., Random Patches, Boosting, Stacking), as well as SVM, Logistic Regression, and Decision Tree classifiers with dimensionality reduction. |
| Unsupervised Learning - Liver Lesion Segmentation | Link | Liver lesion segmentation using CT images from the 3Dircadb dataset, implementing unsupervised learning methods (e.g., KMeans, MiniBatchKMeans, DBScan, and Gaussian Mixture Models (GMM)), as well as performance comparison and parameter optimization techniques. |