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An SVM classifier was used to categorize melanoma images from all the dermoscopic images in the dataset. Using both color and texture parameters, the model obtained the highest accuracy of 96%. Babu et al. [44] proposed an efficient system to detect melanoma skin cancer using Histogram of Gradient (HOG) features.
Dec 8, 2022 · The authors of this study developed a smart classification algorithm and an automated skin lesion segmentation based on dermoscopic images. We ...
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The proposed hybrid pre-trained convolutional neural network and machine learning classifiers are used for feature extraction and classification.
Mar 31, 2023 · Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy.
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The main objective of this paper is to classify the Melanoma and Non-melanoma using dermoscopy images from the Med Node Dataset. The images are enhanced by ...
May 24, 2024 · It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based ...
This paper aims to summarize six widely used algorithms to inform the readers of how and why they are used and the accuracy of melanoma classification using ...
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For the pixel-wise categorization of melanoma lesions, the system uses a multi-stage, multi-scale approach and the SoftMax classifier. We create a brand-new ...
May 13, 2024 · This paper firstly presents the components of machine learning-based skin cancer diagnosis. It then presents the literature review on the current advance in ...
Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer ...