Skin Lesion Diagnosis Using Convolutional Neural Networks
Daniel Alonso Villanueva Nunez Yongmin Li
Cancerous skin lesions are one of the most common malignancies detected in
humans, and if not detected at an early stage, they can lead to death.
Therefore, it is crucial to have access to accurate results early on to
optimize the chances of survival. Unfortunately, accurate results are typically
obtained by highly trained dermatologists, who may not be accessible to many
people, particularly in low-income and middle-income countries. Artificial
Intelligence (AI) appears to be a potential solution to this problem, as it has
proven to provide equal or even better diagnoses than healthcare professionals.
This project aims to address the issue by collecting state-of-the-art
techniques for image classification from various fields and implementing them.
Some of these techniques include mixup, presizing, and test-time augmentation,
among others. Three architectures were used for the implementation:
DenseNet121, VGG16 with batch normalization, and ResNet50. The models were
designed with two main purposes. First, to classify images into seven
categories, including melanocytic nevus, melanoma, benign keratosis-like
lesions, basal cell carcinoma, actinic keratoses and intraepithelial carcinoma,
vascular lesions, and dermatofibroma. Second, to classify images into benign or
malignant. The models were trained using a dataset of 8012 images, and their
performance was evaluated using 2003 images. It's worth noting that this model
is trained end-to-end, directly from the image to the labels, without the need
for handcrafted feature extraction.