Paper Title :Skin Lesion Classification and Prediction - A Comparative Analysis of Machine Learning Techniques
Author :Shemphang Ryntathiang, Bijeesh T V
Article Citation :Shemphang Ryntathiang ,Bijeesh T V ,
(2024 ) " Skin Lesion Classification and Prediction - A Comparative Analysis of Machine Learning Techniques " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 14-20,
Volume-12,Issue-4
Abstract : One of the deadliest types of skin cancer, melanoma, needs to be diagnosed as soon as possible in order to
increase patient survival rates.This study explored the use of machine learning for the automated categorization of cancerous
lesions in dermoscopic images. Models like Convolutional Neural Networks (CNNs), ResNet50, InceptionV3 and
DenseNet121 were tested on the HAM10000 benchmark dataset with and without data augmentation.The primary measures
of performance were accuracy, precision, recall, and F1 score. Out of all the models evaluated, the CNN model achieved the
highest accuracy of 97% with the application of data augmentation. In general, data augmentation proved to greatly improve
the accuracy of the model. Furthermore, efforts were made to further enhance performance by implementing techniques such
as linear discriminant analysis (LDA) and principal component analysis (PCA) to reduce dimensionality. While PCA
decreased accuracy for some models, LDA improved it in select cases. Results demonstrate machine learning, especially
CNNs, can accurately distinguish benign and malignant skin lesions. Automating this screening could help dermatologists
reduce late-stage diagnosis. Widespread adoption of these techniques may lower treatment costs, save lives, and pave the
way for automated diagnosis in other medical imaging areas
Keywords - Machine Learning (ML), Convolutional Neural Networks(Cnns), Skin Cancer, Dermoscopic Images, Principal
Component Analysis (PCA), Linear Discriminant Analysis (LDA)
Type : Research paper
Published : Volume-12,Issue-4
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-20706
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Copyright: © Institute of Research and Journals
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Published on 2024-07-10 |
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