Melanoma Skin Cancer Detection Using Image Processing

Garg, Nishtha; Sharma, Vishakha; Kaur, Prabhjot

doi:10.1007/978-981-10-6614-6_12

Cited by 23 publications

(17 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step in achieving image characteristics for melanoma detection is to diagnose and localize the lesions in the image. Automated melanoma detection systems are based on using one imaging modality (like dermoscopy), computer algorithms and mathematical models to predict if a skin lesion is a melanoma [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A hybrid neural network – world cup optimization algorithm for melanoma detection

2018

View full text Add to dashboard Cite

AbstractOne of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP) employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

show abstract

Section: Introductionmentioning

confidence: 99%

A hybrid neural network – world cup optimization algorithm for melanoma detection

2018

View full text Add to dashboard Cite

show abstract

“…Garg et al [14] proposed a way to detect melanoma skin cancer by using some image processing techniques.…”

Section: Learning Based Approachesmentioning

confidence: 99%

A Deep CNN Model for Skin Cancer Detection and Classification

Junayed¹,

Anjum²,

Sakib³

et al. 2021

CSRN

View full text Add to dashboard Cite

Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.

show abstract

“…They used Naive Bayes classification and SVM and obtained an accuracy of 82.21%. [2][3][4][5] Garg N. et al used image processing approach to detect melanoma. [3][4] Aya Abu Ali et al used CNN for detection of melanoma and obtained an accuracy of 81.1%.…”

Section: Benchmarkingmentioning

confidence: 99%

“…Andre Esteva et al classified melanoma using image processing and obtained an accuracy of 76.9%. 3 Ning Situ et al used bag-of-features approach to detect melanoma based on microscopic imaging with epiluminescence. They used Naive Bayes classification and SVM and got 82.21% accuracy.…”

Section: Introductionmentioning

confidence: 99%