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The aim of this research is to propose a multiclass machine learning model that detect the lesion diagnosis rather than its type. The used dataset was retrieved from the International Skin Imaging Collaboration datasets archive since it is a benchmark that has thousands of dermoscopic images of different diagnoses.
This research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions.
The aim of this research is to propose a multiclass machine learning model that detect the lesion diagnosis rather than its type. The used dataset was retrieved.
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May 30, 2024 · This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features.
In this study, we propose a weighted average ensemble learning-based model to classify seven types of skin lesions. We used five deep neural network models, ...
We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features.
Sep 26, 2023 · In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection.
Oct 28, 2022 · We developed a fully automated approach for detecting and classifying skin lesions using Machine Learning and customized Convolutional Neural ...
This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence.
In this work, a unified CAD model is proposed based on a deep learning framework for skin lesion segmentation and classification.