A robust approach for automatic skin cancer disease classification

Singh, Surya Kant; Jalal, Anand Singh

doi:10.1109/iicip.2016.7975301

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…An strategy for lowering the likelihood of a wrong diagnosis being made is described in [3]. The K-mean Clustering technique is used as the initial step in the data set's preprocessing when following the recommended approach.…”

Section: Literature Surveymentioning

confidence: 99%

Texture analysis for skin cancer diagnosis using dermoscopic images

Nishad¹,

Meenakshi²,

Ramakrishnan³

et al. 2023

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This paper provides a foundation to examine the dermoscopic images for skin cancer diagnosis. A dermoscopic image will often include textured areas that make up a major amount of the image. It is conceivable to organize and categorize such textures according to whether they are related with artifacts or if they reflect biological structure. Given the connection between structure, disease, and texture, it seems likely that quantitative measurements of texture might make it possible to characterize the tissues included inside a dermoscopic image. It has been shown that texture is a valuable characteristic for the characterization of skin cancer in dermoscopic images. The proposed system is comprised of two stages: the first is the extraction of information or features from dermoscopic images, and the second is the categorization of those images using a decision tree classifier. Based on the findings, it is possible to draw the conclusion that the extracted features have kept all of the information presents in the dermoscopic image that provides an overall accuracy of 98.89%

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Section: Literature Surveymentioning

confidence: 99%

Texture analysis for skin cancer diagnosis using dermoscopic images

Nishad¹,

Meenakshi²,

Ramakrishnan³

et al. 2023

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show abstract

“…This model achieved 79.3% AC, 90% DI, 83.3% JA, 89.4% SE and 97.6% SP with an image size of 384 x 512. Pratik Dubal et al, [14], Proposed a methodology to detect and identify skin cancers as benign or malignant based on images taken through a common camera. Images are segmented, attributes are taken out using ABCD rules, and neural networks are formed to classify lesions with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer-Based Skin Carcinoma Detection

Chalapathy¹,

Srikantaiah²

2022

ECS Trans.

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With the emergence of several pollutants, cosmetics, and chemicals into our day-to-day lives, skin cancer is becoming a common disease. Machine learning and image processing is used for identification of type of skin cancer. Several algorithms have been proposed to detect skin cancer, but most of the inputs are fed manually. Manual testing for skin cancer is difficult and strong similarities between different skin types can lead to false detection of lesions classes. To overcome this problem, we propose an algorithm which requires minimal intervention of doctors when provided with an input affected skin image. Images of the affected area are captured with the help of derma scope and fed to the model. The model initially checks whether the image contains a skin tissue that has cancer or not and if it has cancer then classify into which class of cancer it is, whether it is melanoma, nevus, or seborrheic keratosis. Skin cancer segmentation, along with the same evaluation criteria and the results, also showed that the individual accuracy of each class of skin cancer is computed in efficient manner. The models iteratively learn from its past experience and make the model more enhancing. By 2021, there will be 6.3 billion smartphone subscriptions, which might enable low-cost universal access to skin cancer diagnostic services.

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“…Finally, SVM is used for prediction. Skin cancer classification approach in [9] uses k-means technique for skin lesion segmentation. Features are extracted by local binary pattern and color coherence vector.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification System Using Shearlets

Kumar¹,

Kumanan²

2023

Computer Systems Science and Engineering

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The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST's sub-bands, energy features are extracted and stored in the feature database for training. SVM classifier is used for the classification of skin lesion images. The dermoscopic skin images are obtained from PH 2 database which comprises of 200 dermoscopic color images with melanocytic lesions. The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic (ROC) curves. The SLC system achieves 96% classification accuracy using NSST's energy features obtained from 3 rd level with 8-directions.

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A robust approach for automatic skin cancer disease classification

Cited by 14 publications

References 14 publications

Texture analysis for skin cancer diagnosis using dermoscopic images

Texture analysis for skin cancer diagnosis using dermoscopic images

Deep Transfer-Based Skin Carcinoma Detection

Skin Lesion Classification System Using Shearlets

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