Dermoscopic images are widely used for melanoma detection. Many existing works based
on traditional classification methods and deep learning models have been proposed for …

Hair removal is one of the significant challenges before applying the automatic
segmentation and classification process for skin lesions in a computer-aided diagnosis …

A great progress in deep learning technologies for skin cancer detection from dermoscopic
images has been made for a decade. While its performance is vulnerable to a large amount …

In dermoscopic images, various thin artefacts naturally appear, most usually in the form of
hairs. While trying to find the border of the skin lesion, these artefacts effect the lesion …

Dermoscopic images are often contaminated by artifacts including clinical pen markings,
immersion fluid air bubbles, dark corners, and most importantly hair, which makes …

Pattern analysis has been shown to have higher reliability for melanoma and nevus
classification. The ABCD method, which is common to be used as a melanoma diagnosis …

One of the prevalent and severe forms of skin cancer is melanoma. If not caught in time, it
poses a threat to human life. The survival rate may be increased with early identification. For …

Skin cancer is one of the most common forms of cancer in the world that has grown
dramatically over the past decades. Malignant melanoma is the deadliest type of skin …

Medical images play an essential role in detecting and diagnosing numerous diseases. With
different medical imaging modalities, a large volume of images is generated every day …

Dermoscopic image analysis is an important field that helps in skin cancer diagnosis.
Removing image artefacts, such as hair or measuring tools, remains challenging due to the …