Medical image classification is crucial for improving diagnosis and treatment, especially when human analysis is time-consuming and less precise compared to Computer-Aided Diagnosis (CAD) systems. Our study focused on classifying microscopic fungi images, addressing a significant yet frequently overlooked health threat posed by fungal infections. We evaluated the effectiveness of transfer learning using four pre-trained models (VGG16, ResNet50, EfficientNetb0, and ViT16) using various metrics like balanced accuracy, MCC, F1-score, and confusion matrices. We also compared deep learning models with traditional machine learning algorithms such as Logistic Regression, Naive Bayes, and Random Forest. To ensure accurate model assessment, we implemented cross-validation for deep learning models to better assess their performance on unseen data. Notably, our findings reveal that EfficientNet outperformed all other models, achieving a remarkably balanced accuracy of 0.9 when augmented. Additionally, we employed GRAD-CAM for model explainability and visualized the Attention mechanism for ViT. These findings underscore the significant potential of deep learning models in medical image classification and their crucial role in addressing critical healthcare challenges.
Project_Report.pdfis the 25 page report of our study
classic-ml.ipynbcontains code for preprocessing and classifying the images utilizing classical ML algorithmsdata_exploration.ipynbcontains code for the EDA of the datasetfungi-efficientnet-classification_final.ipynbcontains code for the transfer learning of EfficientNetb0visiontransformer.ipynbcontains code for the transfer learning of Vision Transformer Base 16fungivgg16classification.ipynbcontains code for the transfer learning of VGG16fungi-resnet-classification_finalcontains code for the transfer learning of ResNet50
engine.pycontains functions utilized for the training of Pytorch modelshelper-functions.pycontains functions mostly used to visualize the results of classification and training tasks.