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Comparison of Deep Learning Algorithms for Early Detection of Melanoma Skin Cancer on Dermoscopic and Non-dermoscopic Images by Niharika Wamane; Aishwarya Yadav; Jidnyasa Bhoir; Deep Shelke; Deepali Kadam is a book available to read on EtoBox.

What is Comparison of Deep Learning Algorithms for Early Detection of Melanoma Skin Cancer on Dermoscopic and Non-dermoscopic Images about?

Our research suggests a deep-learning method for automated recognition of melanoma utilizing dermoscopic and non-dermoscopic pictures. The methodology entails creating CNN and ResNet-50 models as well as using a number of pre-processing methods. With the use of hair removal and augmentation procedures, the CNN and ResNet-50 models accurately identified melanoma with dermoscopic photographs with 98.07% and 99.83% accuracy, respectively. The CNN and ResNet-50 models used the hair removal method to obtain accuracy for non-dermoscopic images of 97.06% and 100%, respectively. An accuracy of 96.40% was achieved using CNN through the adoption of age and sex as additional criteria in the identification of melanoma in dermoscopic images. The generated models can be a useful tool in the early identification of melanoma, which is essential for its effective treatment as well as the prevention of fatalities. This can be accomplished by combining the models with multiple methods of pre-processing and the inclusion of age and sex.

Author

Niharika Wamane; Aishwarya Yadav; Jidnyasa Bhoir; Deep Shelke; Deepali Kadam

Publisher

Springer International Publishing

Published

2023

Language

EN

ISBN

9783031364013

Subjects

Computer Science, Science, Stem