To build a CNN model to detect melanoma from images.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

To build a CNN based model which can accurately 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 that can evaluate images and alert dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.

The dataset consists of 2357 images of malignant and benign oncological diseases, which were formed from the International Skin Imaging Collaboration (ISIC). All images were sorted according to the classification taken with ISIC, and all subsets were divided into the same number of images, with the exception of melanomas and moles, whose images are slightly dominant.

The data set contains the following diseases:

• Actinic keratosis
• Basal cell carcinoma
• Dermatofibroma
• Melanoma
• Nevus
• Pigmented benign keratosis
• Seborrheic keratosis
• Squamous cell carcinoma
• Vascular lesion

• NumPy
• TensorFlow
• Keras
• Matplotlib
• Seaborn
• Augmentor

A CNN model was built to accurately detect melanoma from a given image.

This project was done as an assignment as part of the Deep Learning module of the Executive PG Programme in Machine Learning & AI - November 2022 - IIIT, Bangalore & Upgrad.

Project done by: Prasanna Rahavendra A