# Multimodal skin lesion classification for early cancer diagnosis using deep learning

## TL;DR

This paper presents a deep learning model that improves skin cancer detection using multiple neural network architectures and achieves high accuracy.

## Contribution

The novel contribution is an ensemble model combining three DCNNs with fine-tuning and hyperparameter optimization for skin lesion classification.

## Key findings

- The ensemble model achieved 97.9% testing accuracy in skin lesion classification.
- The model showed 99.2% precision, recall, and F1-score, outperforming individual models.

## Abstract

Skin cancer, particularly melanoma, is a rapidly spreading and potentially life-threatening disease affecting humans. Melanoma typically begins on the skin’s surface before penetrating deeper layers. Early detection significantly improves survival rates, with simple and cost-effective treatments yielding a 96% success rate. Traditional diagnosis methods rely on expert dermatologists, specialized equipment, and invasive biopsies. Deep learning offers advanced solutions for detecting skin cancer earlier and with high accuracy to mitigate costs and assist dermatologists. Deep Convolutional Neural Networks have shown promise in several computer vision tasks, including image classification, prompting their application in dermatology.

This work focuses on leveraging three prominent DCNN architectures, DenseNet 201, VGG16, and InceptionV3, to classify skin lesions using dermoscopic images. The HAM10000 dataset was taken and divided into training and testing sets. The preprocessing methods include image normalization, scaling, and Otsu’s binary thresholding segmentation and augmentation techniques were applied. We introduced two fine-tuning approaches. Firstly, the top layers of the base model are retrained. Secondly, retraining the half layers of the base models and additional layers are added to form customized CNN models. We merge these underlying models into an ensemble and hyperparameter tuning to enhance performance. The transparency and interpretability of the model are enhanced by Grad-CAM, which raises the model’s dependability for clinical applications.

Combining DenseNet-201, InceptionV3, and VGG16, the proposed ensemble model outperforms the individual models with a testing accuracy of 97.9%. Additionally, it exhibits a better F1-score, recall, and precision of 99.2%, demonstrating its efficacy in automated skin lesion detection.