A Comparative Analysis Towards Melanoma Classification Using Transfer
  Learning by Analyzing Dermoscopic Images
Md. Fahim Uddin Nafisa Tafshir Mohammad Monirujjaman Khan
Melanoma is a sort of skin cancer that starts in the cells known as
melanocytes. It is more dangerous than other types of skin cancer because it
can spread to other organs. Melanoma can be fatal if it spreads to other parts
of the body. Early detection is the key to cure, but it requires the skills of
skilled doctors to diagnose it. This paper presents a system that combines deep
learning techniques with established transfer learning methods to enable skin
lesions classification and diagnosis of melanoma skin lesions. Using
Convolutional Neural Networks, it presents a method for categorizing melanoma
images into benign and malignant images in this research (CNNs). Researchers
used 'Deep Learning' techniques to train an expansive number of photos &
essentially to get the expected result deep neural networks to need to be
trained with a huge number of parameters as dermoscopic images are sensitive &
very hard to classify. This paper, has been emphasized building models with
less complexity and comparatively better accuracy with limited datasets &
partially fewer deep networks so that the system can predict Melanoma at ease
from input dermoscopic images as correctly as possible within devices with less
computational power. The dataset has been obtained from ISIC Archive. Multiple
pre-trained models ResNet101, DenseNet, EfficientNet, InceptionV3 have been
implemented using transfer learning techniques to complete the comparative
analysis & every model achieved good accuracy. Before training the models, the
data has been augmented by multiple parameters to improve the accuracy.
Moreover, the results are better than the previous state-of-the-art approaches
& adequate to predict melanoma. Among these architectures, DenseNet performed
better than the others which gives a validation accuracy of 96.64%, validation
loss of 9.43% & test set accuracy of 99.63%.