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Auto-PCOS-Classification

Auto-PCOS Classification Challenge Team LearnDroids

Ultrasound Image Classification Pipeline

Brief Write-up and Pipeline Overview

This repository contains the code for a machine learning pipeline to classify ultrasound images into healthy and unhealthy categories using a MobileNet-based model. The pipeline includes data preprocessing, model training, and evaluation.

Pipeline Overview

  1. Data Preprocessing:

    • The image data is read from an Excel sheet containing class labels.
    • Images are preprocessed using the MobileNet preprocessing function, including augmentation techniques like zoom, shear, and horizontal flip.

  2. Model Architecture:

    • The base MobileNet model is utilized, with the final layer modified for binary classification.
    • The model is compiled using the Adam optimizer, binary cross-entropy loss, and multiple evaluation metrics (Recall, Accuracy, Precision, and AUC).

  3. Training:

    • The model is trained using the specified dataset, with checkpoints and early stopping callbacks.

Results on Validation Dataset

Evaluation Metrics Table

Loss Recall Accuracy Precision AUC
1.5586 0.6641 0.8146 0.6591 0.8049