In this repository, we aim to build a CNN based model which can detect melanoma. The object of the project is to undertand development of a CNN model using Keras and understand various strategies that will come up when building neural networks, including solving for underfitting, overfitting and class imbalance.
Data Reading/Data Understanding → Defining the path for train and test images
Dataset Creation→ Create train & validation dataset from the train directory with a batch size of 32 and image size of 180*180
Dataset visualisation → Create a code to visualize one instance of all the nine classes
Model Building & training
- Create a CNN model, which can accurately detect 9 classes present in the dataset.
Chose an appropriate data augmentation strategy to resolve underfitting/overfitting if present
Model Building & training on the augmented data :
- Create a CNN model, which can accurately detect 9 classes present in the dataset.
- Findings after the model fit to identify if the model is still under or overfit.
Class distribution: Examining the class distribution in the dataset
Handling class imbalances: Using the Augmentor library to increase the number of sample images for specific classes by augmenting data.
Model Building & training on the rectified class imbalance data :
- Create a CNN model, which can accurately detect 9 classes.
- Findings after model fit to identify whether or not our strategies have helped to increase the performance of the model.
- pathlib
- matplotlib
- numpy
- pandas
- os
- PIL
- tensorflow
- keras
- glob
Created by [@justinhtn]