An accurate and noninvasive skin cancer screening based on imaging technique
G Rajput, S Agrawal, G Raut… - International Journal of …, 2022 - Wiley Online Library
In the last decade, the public health problem is the primary consciousness worldwide, and
cancer is especially the central issue. Further, skin cancer comes in the top‐3 of the world's …
cancer is especially the central issue. Further, skin cancer comes in the top‐3 of the world's …
Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand …
Background Previous studies of artificial intelligence (AI) applied to dermatology have
shown AI to have higher diagnostic classification accuracy than expert dermatologists; …
shown AI to have higher diagnostic classification accuracy than expert dermatologists; …
Robust feature spaces from pre-trained deep network layers for skin lesion classification
FP dos Santos, MA Ponti - 2018 31st SIBGRAPI Conference on …, 2018 - ieeexplore.ieee.org
The incidence of skin cancer in the world population is a public health concern, and the first
diagnosis takes into account the appearance of lesions on skin. In this context, automated …
diagnosis takes into account the appearance of lesions on skin. In this context, automated …
Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation
CK Juan, YH Su, CY Wu, CS Yang, CH Hsu… - Scientific reports, 2023 - nature.com
We aimed to develop an accurate and efficient skin cancer classification system using deep-
learning technology with a relatively small dataset of clinical images. We proposed a novel …
learning technology with a relatively small dataset of clinical images. We proposed a novel …
The role of machine learning and deep learning approaches for the detection of skin cancer
Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized
care. The development of ML algorithms in dermatology has been supported lately …
care. The development of ML algorithms in dermatology has been supported lately …
Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models
Artificial intelligence models match or exceed dermatologists in melanoma image
classification. Less is known about their robustness against real-world variations, and …
classification. Less is known about their robustness against real-world variations, and …
AI-powered diagnosis of skin cancer: a contemporary review, open challenges and future research directions
Simple Summary The proposed research aims to provide a deep insight into the deep
learning and machine learning techniques used for diagnosing skin cancer. While …
learning and machine learning techniques used for diagnosing skin cancer. While …
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
Bcn20000: Dermoscopic lesions in the wild
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of
skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl\'inic in Barcelona …
skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl\'inic in Barcelona …
Deep ensemble learning for skin lesion classification from dermoscopic images
AH Shahin, A Kamal, MA Elattar - 2018 9th Cairo International …, 2018 - ieeexplore.ieee.org
Skin cancer is one of the leading causes of death globally. Early diagnosis of skin lesion
significantly increases the prevalence of recovery. Automatic classification of the skin lesion …
significantly increases the prevalence of recovery. Automatic classification of the skin lesion …