DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer

Karargyris, Alexandros; Karargyris, Orestis; Pantelopoulos, Alexandros

doi:10.1109/ictai.2012.180

Cited by 39 publications

(23 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Smartphone-based skin cancer recognition remains a challenging area of research, and this has slowed the commercialization and general availability of portable melanoma screening devices. Some of the technological limitations of the previously reported methods include: (a) the application used a non-smartphone-based camera with a dermatoscope to photograph lesions [13,14,19,20,29], (b) the application was tested on small image set or different databases, making it difficult to directly compare the results [15,16,19,22], (c) the application demonstrated a single feature extraction [2,30,60], (d) the application ran only on a desktop and thus was not considered real-time [13,19,21,25,54], and (e) the application had average accuracy, sensitivity, or selectivity [22,50,51]. We showed that our smartphone application is able to overcome the above limitations with better or comparable computation times and accuracies to those reported earlier.…”

Section: Discussionmentioning

confidence: 99%

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

et al. 2019

View full text Add to dashboard Cite

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Karargyris et al [30] proposed a new application for image processing to identify skin malignancy using a 2…”

Section: Related Workmentioning

confidence: 99%

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Abdulhamid

Şahiner

Rahebi

2020

BioMed Research International

View full text Add to dashboard Cite

In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.

show abstract

“…A recent work 6 is composed by an app developed for iOS, installed in an iPhone 4/4S integrated with a microscope of 60x zoom capability. Basically, this app makes a color transformation from Red-Green-Blue (RGB) color space to a Hue Saturation Value color space, with a further transformation of the image to binary.…”

Section: Related Projectsmentioning

confidence: 99%

Melanoma detection using a mobile phone app

Diniz

Ennser

2016

SPIE Proceedings

View full text Add to dashboard Cite

Mobile phones have had their processing power greatly increased since their invention a few decades ago. As a direct result of Moore's Law, this improvement has made available several applications that were impossible before. The aim of this project is to develop a mobile phone app, integrated with its camera coupled to an amplifying lens, to help distinguish melanoma. The proposed device has the capability of processing skin mole images and suggesting, using a score system, if it is a case of melanoma or not. This score system is based on the ABCDE signs of melanoma, and takes into account the area, the perimeter and the colors present in the nevus. It was calibrated and tested using images from the PH2 Dermoscopic Image Database from Pedro Hispano Hospital. The results show that the system created can be useful, with an accuracy of up to 100% for malign cases and 80% for benign cases (including common and atypical moles), when used in the test group.

show abstract

DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer

Cited by 39 publications

References 6 publications

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Melanoma detection using a mobile phone app

Contact Info

Product

Resources

About