maniteja6799/melanoma_detection

Melanoma Detection using a CNN model

CNN based model which can detect melanoma.

Table of Contents

• General Information
• Data characteristics
• Predicted models
• Technologies Used
• Contact

General Information

This project is to create a CNNbased model to detect Melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution which can evaluate images and alert the dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

Dataset characteristics

Dataset contains a total of ~2.2k images belonging to 9 classes.

Dataset is loaded as a Tensorflow Batch Dataset. The image_batch is a tensor of the shape (32, 180, 180, 3). This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images.

Predicted models

1. Create a Basic CNN model, with two sets of convolution layers followed by 2 dense layers, ending with a softmax layer with 9 outcomes.
2. Adding droput layers
3. testing the change in accuracy with adding more layers and increasing the filter size.
4. Agumenting image data to deal with class imbalance

Technologies Used

• Python 3.10.9
• Jupyterlab 3.6.3
• numpy 1.23.5
• pandas 1.5.3
• matplotlib 3.7.0
• tensorflow 2.10.0

Contact

Created by Pawan Mani Teja Kuppili