A SVM-based diagnosis of melanoma using only useful image features

Mustafa, Suleiman; Kimura, Akio

doi:10.1109/iwait.2018.8369646

Cited by 31 publications

(16 citation statements)

References 4 publications

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“…All the images of the database were selected on the basis of their quality, resolution and dermoscopic features. Good results were obtained also in [7] on the same data set Ph 2 , On the other hand, the objective in [34] was to select the most important image features usable in melanoma detection. In this case, differently from [33] where a high quality data set was adopted, the authors performed their experimentations on a data set constituted by plain photographs publicly available from two online databases https://www.dermquest.coml and http://www.dermins.netl, obtaining very good results in terms of sensitivity (96.77%) and, consequently, in terms of F-score (90.91%).…”

Section: Numerical Experimentationsmentioning

confidence: 90%

“…In this section a preliminary numerical comparison between two different approaches (Support Vector Machine and Multiple Instance Learning) is reported, using color and color/texture features. This study starts from some considerations related to the works [7,33,34]. In [33] the authors analyzed the role played by the color and the texture features, showing empirically that using only the color features outperforms the use of the texture features: very good results were obtained by means of different type of classifiers on a image data set drawn from the Ph 2 database [8], containing 200 melanocytic lesions images (80 common nevi, 80 atypical nevi and 40 melanomas).…”

Section: Numerical Experimentationsmentioning

confidence: 99%

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Melanoma detection using color and texture features in computer vision systems

Fuduli¹,

Veltri²,

Vocaturo³

et al. 2019

Adv. sci. technol. eng. syst. j.

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All forms of skin cancer are becoming widespread. These forms of cancer, and melanoma in particular, are insidious and aggressive and if not treated promptly can be lethal to humans. Effective treatment of skin lesions depends strongly on the timeliness of the diagnosis: for this reason, artificial vision systems are required to play a crucial role in supporting the diagnosis of skin lesions. This work offers insights into the state of the art in the field of melanoma image classification. We include a numerical section where a preliminary analysis of some classification techniques is performed, using color and texture features on a data set constituted by plain photographies, to which no pre-processing technique has been applied. This is motivated by the necessity to open new horizons in creating self-diagnosis systems for accessible skin lesions, due also to a huge innovation of cameras, smartphones technology and wearable devices.

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Section: Numerical Experimentationsmentioning

confidence: 90%

Section: Numerical Experimentationsmentioning

confidence: 99%

Melanoma detection using color and texture features in computer vision systems

Fuduli¹,

Veltri²,

Vocaturo³

et al. 2019

Adv. sci. technol. eng. syst. j.

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“…Diagnosing melanoma using SVM having only useful image features was the work of Mustafa, S (2018). RBF kernel is the first hyper parameters having C for cost parameter and Y for kernel parameter.…”

Section: Related Literaturementioning

confidence: 99%

“…S(i,j) calculation was done with 15 features extraction. This being the condition, exhaustive grid search was done to determine the optimum turning parameters C and Y that would be required for improving SVM-RBF performance [38].…”

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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“…Besides most groups trust in CNNs in recent researches, some are still using other methods. Mustafa and Kimura followed an approach for melanoma classification based on manually developing and selecting features [6]. Lesions of interest are segmented using the GrabCut algorithm.…”

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confidence: 99%

DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images

et al. 2020

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Background: Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential to surviving this type of cancer. Results: An approach to classify skin lesions using deep learning for early detection of melanoma in a case-based reasoning (CBR) system is proposed. This approach has been employed for retrieving new input images from the case base of the proposed system DePicT Melanoma Deep-CLASS to support users with more accurate recommendations relevant to their requested problem (e.g., image of affected area). The efficiency of our system has been verified by utilizing the ISIC Archive dataset in analysis of skin lesion classification as a benign and malignant melanoma. The kernel of DePicT Melanoma Deep-CLASS is built upon a convolutional neural network (CNN) composed of sixteen layers (excluding input and ouput layers), which can be recursively trained and learned. Our approach depicts an improved performance and accuracy in testing on the ISIC Archive dataset. Conclusions: Our methodology derived from a deep CNN, generates case representations for our case base to use in the retrieval process. Integration of this approach to DePicT Melanoma CLASS, significantly improving the efficiency of its image classification and the quality of the recommendation part of the system. The proposed method has been tested and validated on 1796 dermoscopy images. Analyzed results indicate that it is efficient on malignancy detection. BackgroundAlthough case-based reasoning (CBR) has been applied in a number of medical systems, only a few systems have been developed for melanoma. The estimated 5-year survival rate for patients whose melanoma is detected early is about 99% [1] in the USA and around 93% [2] in Germany. The survival rate falls to 63% when the disease reaches the lymph nodes and 20% when the disease metastasizes to distant organs [1]; therefore, having the right information

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A SVM-based diagnosis of melanoma using only useful image features

Cited by 31 publications

References 4 publications

Melanoma detection using color and texture features in computer vision systems

Melanoma detection using color and texture features in computer vision systems

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

DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images

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