Dermatologist vs Neural Network

TL;DR

This paper presents a convolutional neural network trained on the HAM10000 dataset that outperforms pretrained models in detecting melanoma skin cancer with 89% accuracy, aiming to aid resource-limited settings.

Contribution

The study introduces a CNN model that surpasses existing pretrained models in skin cancer detection accuracy using the HAM10000 dataset.

Findings

CNN achieved 89% accuracy in melanoma detection

Model outperforms all pretrained models tested

Potential to assist in resource-limited dermatology settings

Abstract

Cancer, in general, is very deadly. Timely treatment of any cancer is the key to saving a life. Skin cancer is no exception. There have been thousands of Skin Cancer cases registered per year all over the world. There have been 123,000 deadly melanoma cases detected in a single year. This huge number is proven to be a cause of a high amount of UV rays present in the sunlight due to the degradation of the Ozone layer. If not detected at an early stage, skin cancer can lead to the death of the patient. Unavailability of proper resources such as expert dermatologists, state of the art testing facilities, and quick biopsy results have led researchers to develop a technology that can solve the above problem. Deep Learning is one such method that has offered extraordinary results. The Convolutional Neural Network proposed in this study out performs every pretrained models. We trained our model on the HAM10000 dataset which offers 10015 images belonging to 7 classes of skin disease. The model we proposed gave an accuracy of 89%. This model can predict deadly melanoma skin cancer with a great accuracy. Hopefully, this study can help save people's life where there is the unavailability of proper dermatological resources by bridging the gap using our proposed study.