Detection of melanoma using distinct features

Satheesha, T Y; Satyanarayana, D.; Giriprasad, M. N.; Nagesh, K. N.

doi:10.1109/icbdsc.2016.7460367

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…In table 1 can be seen some of the images used for the analysis with the order given by the random number generator, with their respective test within the network mounted, and the evaluation of the result on the basis of the actual values of classification, the set of test images is obtained from the project MED-NODE[7] and the international collaboration of melanoma images project (ISIC) [17] The notations used in the table are: Comparing the level of effectiveness of the implemented neural network with techniques as Delaunay triangulation [18] arises where a percentage of success of 66.7% for Melanoma images, greater efficiency is presented in the accuracy of records of melanoma, about to the approach by natural computing technique [19], similar results are gotten, with a percentage of success of 80%, the detection of melanoma through geometric characteristics project [20] obtained a level of success to 89% with a rate of success higher than the proposed project, the technique of color correlogram [5] has a level of 91.5% efficiency with the use of a Bayesian classifier, as same than the segmentation technique for classification of the nearest neighbors [21] presenting even a level of highly superior efficiency compared to other work of the project and the proposed system, however these projects are analyzed only with efficient lighting condition images, leaving in doubt the level of efficiency in other conditions. In the presented context, results obtained in the study presented a suitable percentage of approximation of 77.50% through the structure of the proposed network, however, other techniques far outweigh it, being necessary to specify that performance tests are performed using different conditions and ideal illimunation states, artificial neural networks are one of the techniques of most renowned for the digital processing of images, every day new techniques and models based on different algorithms that can exceed the normal functioning of these and their efficiency in the classification of images appear.…”

Section: Resultsmentioning

confidence: 61%

Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks

Mahecha

Parra

Velandia

2018

Lecture Notes in Computer Science

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Skin cancer is one of the most important challenges in modern medicine, especially skin melanoma, being the main causer of deaths for this disease. Images analysis is one of the most transcendental techniques for Melanoma early detection as a prevention method. Artificial neural networks are one of the many developed techniques for images digital processing and characteristic similarities detection. In this work a graphic processing unit (GPU) is developed for clinical skin images analysis getting through an artificial neural networks system for similar patterns detection through processing in a collection of modules tasked of silhouette detection of the object to analyze into the image, and tasked to study borders or contour to determinate a final diagnostic, the dataset used for the training of the artificial neural network designed is gotten from the MED-NODE project and project of international skin images collaboration (ISIC) with 730 images of positive and negative cases as full, the proposed system presents finally an accuracy level of 76.67%, with a level of success of 78.79% in melanoma specific cases, and 74.07% in benign lesions cases.

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Section: Resultsmentioning

confidence: 61%

Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks

Mahecha

Parra

Velandia

2018

Lecture Notes in Computer Science

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“…In segmentation of skin lesion, is one of the most obvious difficulties to find out the outline of the lesion from the noisy image. To deal with this problem, an active contour method (also known as snakes), is often utilized to draw an object outline from a 2D image (Abbas, Fondón, & Rashid, ; Satheesha, Satyanarayana, Giriprasad, & Nagesh, ). The border detection accuracy can be improved by fuzing the results taken from multiple segmentation techniques (Do, Zhou, Zheng, Cheung, & Koh, ; Wen, Ming, & Chen, ).…”

Section: Related Workmentioning

confidence: 99%

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Nasir

Khan

Sharif

et al. 2018

Microscopy Res & Technique

153

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Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.

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“…When deoxyribonucleic acid or DNA in skin cells is damaged, the modifications or abnormalities that follow allow the skin cells to expand quickly and develop into deadly tumors. Skin malignancies are commonly diagnosed through physical examination and surgery [2]. This operation is a simple outpatient procedure in which the entire or a portion of the afflicted region is removed and tested at a testing facility.…”

Section: Introductionmentioning

confidence: 99%

Hybrid transformation with 1’st order statistics for medical image analysis and classification

George,

Abdullah,

Abdulhassan

et al. 2023

Comput Sci Inf Technol

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Skin cancer, one of the most critical forms of cancer, required early detection and documentation for efficient treatment, especially as certain types are fatal. In this study, an artificial neural network (ANN) was utilized to discover and index diverse melanomas using the ISIC 2018 dataset. The pre-processing phase is stringent as it insulates the cancerous fraction of a skin image, involving removing, trimming, thinning, and normalizing. In this phase, unwanted hair pieces on the image are eliminated in this phase. Feature extraction from the clipped image is achieved using a discrete cosine transform (DCT) and a gradient transform to transform it into frequency domain coefficients. Statistical feature extraction is used to reduce the amount of data required for ANN training. A dataset from ISIC 2018 that consists of seven different images from dermoscopic procedures for classification purposes is used in the empirical investigation. An accuracy of 85.44% for DCT in the sub-bands and 76.07% for the sub-band gradient transform was achieved by the applied ANN. The hybrid system's mean squared error (MSE) was discovered to be 3.52×10-4. The work highlights the potential of ANN in the early detection of skin cancer, supporting more efficient treatment and preventing advanced cases.

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Detection of melanoma using distinct features

Cited by 22 publications

References 6 publications

Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks

Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Hybrid transformation with 1’st order statistics for medical image analysis and classification

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