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Recognizing Pigmented Skin Lesion based on LightGBM Classifier and Deep Saliency Segmentation using Convolutional Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 39 |
| Year of Publication: 2022 |
| Authors: Ramya J., Vijayalakshmi H.C., Huda Mirza Saifuddin |
10.5120/ijca2022922504
|
Ramya J., Vijayalakshmi H.C., Huda Mirza Saifuddin . Recognizing Pigmented Skin Lesion based on LightGBM Classifier and Deep Saliency Segmentation using Convolutional Neural Network. International Journal of Computer Applications. 184, 39 ( Dec 2022), 39-44. DOI=10.5120/ijca2022922504
Abstract
Malignant melanoma is the most detrimental type of skin cancer which has affected many people worldwide. Melanoma could be cured if diagnosed and treated early. However, it is a difficult to identify melanoma conditions at earlier stages due to data imbalance, large inter-class similarity, and high intra-class variations etc. On the other hand, manual diagnosis of melanoma is more prone to human error. Therefore, a novel strategy to identify and classify pigmented skin lesions using a deep learning approach is presented in this paper. The proposed approach uses a LightGBM classifier and convolutional neural network (CNN) for timely recognition and classification of skin lesions. The classification model is designed based on the saliency evaluation and selection of discriminant features where CNN is employed for performing saliency segmentation. The performance of the proposed classifier is measured using different evaluation metrics and results have validated the efficacy of the classifier for accurate classification of pigmented skin lesions.
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