A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images

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...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Md Fahim Uddin, Tafshir, Nafisa, Khan, Mohammad Monirujjaman
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Cancer
Classification
Comparative analysis
Datasets
Deep learning
Lesions
Machine learning
Mathematical models
Medical imaging
Melanoma
Model accuracy
Neural networks
Parameter sensitivity
Skin cancer
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Summary: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%.
ISSN:2331-8422