Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification

TL;DR

This paper proposes a method to improve the interpretability of deep learning models for skin cancer diagnosis in Bangladesh by using saliency and attention maps to visualize decision-making features.

Contribution

It introduces a novel approach combining saliency and attention maps to enhance transparency in deep learning skin cancer classification models in resource-limited settings.

Findings

Improved interpretability of skin cancer classification models.

Enhanced visualization of critical diagnostic features.

Potential for better clinical acceptance and trust.

Abstract

With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial for enhancing patient outcomes; however, Bangladesh faces a shortage of dermatologists and qualified medical professionals capable of diagnosing and treating skin cancer. As a result, many cases are diagnosed only at advanced stages. Research indicates that deep learning algorithms can effectively classify skin cancer images. However, these models typically lack interpretability, making it challenging to understand their decision-making processes. This lack of clarity poses barriers to utilizing deep learning in improving skin cancer detection and treatment. In this article, we present a method aimed at enhancing the interpretability of deep learning models for skin cancer classification in Bangladesh. Our technique employs a combination of saliency maps and attention maps to visualize critical features influencing the model's diagnoses.