Malaria-cell-detection

• This notebook focuses on developing ML models and for the analysis of the "Malaria Cell Images Dataset" obtained from Kaggle which can be found here.
• The aim of this project is to develop a deep learning model to reliably distinguish the numerous photos of malaria-infected and uninfected cells in the dataset.

Sections

• The notebook is divided into a number of sections, including an exploratory data analysis (EDA), model selection, data pre-processing, model construction, training, and validation, hyperparameter tweaking, model evaluation, presentation of results, and critical analysis and discussion.

Models Used

• The models used in this notebook are as follows:
  • A CNN model with dropout layers, data augmentation, and batch normalization
  • A CNN model with dropout layers, data augmentation, batch normalization, and transfer learning (VGG16)
  • A CNN model with dropout layers, data augmentation, batch normalization, and transfer learning (EfficientNetB07)

Disclaimer

• The python verion used in this notebook is 3.6.8 on a conda environment with GPU acceleration.
• This notebook was submitted as an assignment during my course at northumbria university, kindly do not plagiarize.