Abstract:
Skin cancer is one of the most dangerous health issues in the world. A huge number
of people are affected every year. There are three types of skin cancer, and the most
common one is melanoma, which is also the most dangerous skin cancer with a higher
chance of metastasis and death. Roughly 1-2% of cases of skin cancer are caused by
melanoma and affect 3-5% of the population. Current diagnostic methods are
time-consuming and prone to human error, which requires more precise and effective
detection methods. The goal of this project is to build a deep learning model
architecture that can identify melanoma with high accuracy and low time
consumption, utilizing the ISIC 2019-2020 Melanoma dataset. Our model aims to
improve the accuracy of advanced image processing with deep learning techniques,
which could facilitate early intervention and potentially life-saving treatments. The
ISIC 2019-2020 datasets provide a huge quantity of dermoscopic images, which can
serve as an excellent foundation for training and validating our deep-learning models.
We use some recent famous and well-performed convolutional neural networks
(CNNs) models and train the models, such as ResNet50, ConvNeXt, EfficientNetV2,
VGG16, MobileNetV3, and Swin Transformer, to extract features indicative of
melanoma skin cancer images and predict results accurately based on all these
model’s prediction accuracy. We also created a web-based application to do the cancer
prediction by comparing the results of all these 6th models to get an accurate result.
This study shows the essential role of technology: to keep coming up with new ideas
for advancing medical diagnostics to fight against the deadliest melanoma skin cancer.
We tried to build up a deep-learning model that can detect exact skin cancer cells with
high accuracy and low time consumption based on the ISIC 2019-2020 melanoma
dataset.
