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Anomaly Detection in Medical Images: Python, TF, and PyTorch

In this project, we’ll learn to use deep learning algorithms to automate the screening process and reduce the workload on medical professionals, while improving the accuracy and speed of diagnosis via image classification.

Anomaly Detection in Medical Images: Python, TF, and PyTorch

You will learn to:

Preprocess and augment the medical images.

Develop and train deep learning models for image classification.

Evaluate models using various performance metrics and visualization tools.

Fine-tune models to utilize pretrained neural networks.


Machine Learning

Deep Learning

Data Augmentation

Image Augmentation


Basic understanding of Python

Basic understanding of TensorFlow

Basic understanding of PyTorch

Basic understanding of data visualization using Python





Project Description

Medical imaging is a critical field that allows healthcare professionals to see inside the human body to detect, diagnose, and treat diseases. It has revolutionized the field of medicine and improved the quality of healthcare delivery. However, the task of analyzing these images is still largely manual, requiring specialized knowledge and skill from radiologists.

In this project, we’ll build a deep learning model that will automatically detect abnormalities in these images. This will save a significant amount of time and improve the accuracy of diagnoses.

By the end of this project, you’ll be able to:

  • Understand and apply image preprocessing and enhancement techniques for medical images.

  • Extract and select appropriate features for computer vision tasks.

  • Understand and implement anomaly detection algorithms and techniques.

  • Develop and train deep learning models for image classification using TensorFlow and PyTorch.

  • Evaluate and tune model performance using various metrics and visualization tools.

  • Understand the project management aspects of data preparation, model development, and deployment.

The project is split into several steps, each of which involves a key aspect of the development of our machine learning model:

  1. Gathering and preparing the dataset: We’ll start by sourcing a suitable dataset containing both normal and abnormal medical images. The images will then be preprocessed and prepared for our model.

  2. Feature extraction and selection: We’ll extract relevant features from the images that our model can learn from and select the most important ones to include in our model.

  3. Model development and training: We’ll build and train our model using deep learning frameworks such as TensorFlow and PyTorch. We’ll experiment with different architectures and parameters to find the most effective solution.

  4. Evaluation and tuning: We’ll evaluate the performance of our model and fine-tune it to improve its accuracy and efficiency. We’ll also discuss various metrics used for evaluating the performance of our models.

  5. Deployment: Finally, we’ll discuss how to deploy our model in a real-world setting, allowing it to be used to assist healthcare professionals in diagnosing diseases from medical images.

Project Tasks


Prepare the Data

Task 0: Get Started

Task 1: Import the Libraries

Task 2: Load the Dataset

Task 3: Visualize the Images

Task 4: Preprocess the Images

Task 5: Split the Data

Task 6: Augment the Images


Build an Image Classifier with TensorFlow

Task 7: Set Up a Neural Network

Task 8: Define the Model Architecture

Task 9: Prepare the Model for Training

Task 10: Train and Monitor the Network

Task 11: Evaluate the Performance


Build an Image Classifier with PyTorch

Task 12: Set Up a Neural Network

Task 13: Define the Model Architecture

Task 14: Prepare the Model for Training

Task 15: Train and Monitor the Network

Task 16: Evaluate the Performance


Perform Fine-Tuning and Transfer Learning

Task 17: Fine-Tune the Pretrained TensorFlow Model

Task 18: Fine-Tune the Pretrained PyTorch Model