[PDF] Hair Segmentation and Removal in Dermoscopic Images Using Deep Learning | Semantic Scholar (2024)

Results shows the superiority of the proposed methods compare to other state-of-the arts methods for Melanoma lesions detection.

Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin and it is important to eliminate the hair to get accurate results.

Quantitative and qualitative evaluations show not only that the proposed multi-scale dual-modality strategy is much robust to reconstruct hair-shaped missing regions compared to the existing transformer-based image inpainting method called BAT-Fill, but also that the framework outperforms the state-of-the-art image inPainting models in removing hairs from hairy dermoscopic images.

The automatic identification of melanoma using digital image processing is the focus of this research study and the Black Hat filter, which recognizes and eliminates hair from dermoscopy images automatically is used.

The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case it was possible to get accuracy up to 88.75 %.

Experimental result shows the improvement in classification accuracy, sensitivity and specificity compared with the state of art methods.

The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification and holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes.

The goal of this paper is to provide a skin cancer diagnosis system that can automatically identify lesions as malignant or benign, and some of the methods used in an automated melanoma diagnosis.

The proposed optimization algorithm is suitable for extracting large and complex dermoscopic images to extract image features and it also stops preventing from the entire surface.

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The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% as compared to the DullRazor's approach.

This work provides an effective hair removal algorithm for dermoscopic imagery employing soft color morphology operators able to cope with color images and compares it with other state-of-the-art algorithms.

This work proposes a novel method for simultaneously enhancing both light and dark hairs with variable widths, from dermoscopic images, without the prior knowledge of the hair color, and validate and compare it to other methods.

A hair removal benchmark of six state-of-the-art algorithms, each with a different approach to segment and inpaint the hair pixels, and a series of performance measures that evaluate the similarity between the original hairless image and the one obtained by each of the algorithms.

A robust CAD system based on transfer learning and multi-layer feature fusion network to diagnose complex skin diseases and demonstrates that the proposed approach can dramatically improve the performance of CAD systems based on the conventional recognition and classification algorithms for skin lesion recognition in dermoscopic data.

Surface microscopy does not allow 100% sensitivity in diagnosing invasive melanoma and therefore cannot be used as the sole indicator for excision.

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