Pigment network detection in dermatoscopic images for melanoma diagnosis

Barhoumi, Walid; Baâzaoui, Abir

doi:10.1016/j.irbm.2013.12.010

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…This scheme reduction in pigment network holes' wrong classification. This falls under the ROC curve which is of 0.821 for doing correct distinguishing of lesions without and with pigment network [14].…”

Section: Related Literaturementioning

confidence: 99%

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Mohammed¹,

Mukti²

2019

IJCA

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Early diagnosis of skin cancer is essential health requirement for the patient and a critical task for the dermatologist. The factual thinking is that the chance of patient's survival is high if diagnosed early. Analysis of the skin images and dermoscopy is a mandatory for medical professionals to take appropriate decision on treatment. A number of methods have been researched to use automated and computerized system for skin diseases image processing. Various dermoscopy image processing techniques have been reviewed to explore the possible solution to skin diseases and to select an appropriate method for early detection7 of skin diseases. This review work will be a pathway to scientist, research scholars and medical practitioners.

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Section: Related Literaturementioning

confidence: 99%

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Mohammed¹,

Mukti²

2019

IJCA

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“…Most studies have been proposed for the pigmented network detection [100,111,163]. In addition, other studies have considered feature extracted from patterns for discriminating between benign and malignant skin lesions [63,64,77].…”

Section: Skin Lesion Classification Performancementioning

confidence: 99%

Computational methods for pigmented skin lesion classification in images: review and future trends

Oliveira

Papa

Pereira

et al. 2016

Neural Comput & Applic

168

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Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

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“…Prior to the advancement of deep learning techniques, a typical system using classical pattern recognition consists of the following three steps: segmentation of tumor areas [3], [4], feature extraction based on medical knowledge, and the final classification [5]- [7]. There have also been some attempts to estimate well-known medical indicators [8]- [11], such as the ABCD rule [12] and the 7-point checklist [13].…”

Section: Introductionmentioning

confidence: 99%

Bulk Production Augmentation Towards Explainable Melanoma Diagnosis

Obi

Huu

Umegaki‐Arao

et al. 2021

Preprint

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Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data. In this paper, we propose bulk production augmentation (BPA) to generate high-quality, diverse pseudo-skin tumor images with the desired structural malignant features for additional training images from a limited number of labeled images. The proposed BPA acts as an effective data augmentation in constructing the feature detector for the atypical pigment network (APN), which is a key structure in melanoma diagnosis. Experiments show that training with images generated by our BPA largely boosts the APN detection performance by 20.0 percentage points in the area under the receiver operating characteristic curve, which is 11.5 to 13.7 points higher than that of conventional CycleGAN-based augmentations in AUC.

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Pigment network detection in dermatoscopic images for melanoma diagnosis

Cited by 12 publications

References 22 publications

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Computational methods for pigmented skin lesion classification in images: review and future trends

Bulk Production Augmentation Towards Explainable Melanoma Diagnosis

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