Fractal Model for Skin Cancer Diagnosis Using Probabilistic Classifiers

Jacob, Stalin; Rosita, Jenifer Darling

doi:10.29284/ijasis.7.1.2021.21-29

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…The SVM classification efficiency is evaluated by using 10 fold cross validation. The sensitivity, specificity and accuracy [23][24][25][26] are calculated by using the correct identification of skin image samples. The sensitivity, specificity and accuracy is given by,…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Skin Lesion Classification System Using Shearlets

Kumar¹,

Kumanan²

2023

Computer Systems Science and Engineering

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“…Combining two and three-dimensional wavelet features might aid in the earlier detection of melanoma, the fatal kind of skin cancer. The differential box-counting approach with a fractal feature-based probabilistic classifier is explained in the study of Jacob and Rosita [22]. The fractal properties are obtained using a differential box-counting method.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

Pitchiah¹,

Rajamanickam²

2022

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Skin cancer is the most prevalent and deadliest kind of cancer. Melanoma is the most dangerous type of skin cancer, but it can be detected earlier and successfully treated. The dermoscopic image classification using Machine Learning (ML) approaches in identifying melanoma is increased over the last two decades. The proposed classification system involves three stages. Initially, the pre-processing employs the median filter and thresholding approach aids to remove the hairs and unwanted noise. Then, the shape components, Asymmetry, Border Irregularity, Colour and Dermoscopic structure (ABCD) rule, and Grey Level Co-occurrence Matrix (GLCM) features are utilized to extract the skin lesion region. After that, the K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers are employed to perform melanoma skin classification from the skin lesion. The dermoscopic skin images used in this study are obtained from the PH2 database. Finally, the SVM classifier outperformed the other classifiers in identifying melanoma skin cancer, providing 94.81% efficiency.

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“…The true class probability-based confidence level is employed for the segmentation. A fractal-based system is discussed in [15] for skin cancer diagnosis. It uses differential box-counting to obtain the fractal dimension, and parametric and non-parametric classifiers are employed for skin cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Murugesan¹,

Jeyapriya²,

Hemalatha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3, 5, 7, and 9). An inception network with batch normalization is employed to segment melanoma regions effectively. Different loss functions such as Mean Absolute Error (MAE), Cross Entropy Loss (CEL), and Dice Loss (DL) are utilized for performance evaluation of the TTS system. The anchor box technique is employed to detect the melanoma region effectively. The TTS system is evaluated using 200 dermoscopic images from the PH 2 database. The segmentation accuracies are analyzed in terms of Pixel Accuracy (PA) and Jaccard Index (JI). Results show that the TTS system has 90.19% PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

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Fractal Model for Skin Cancer Diagnosis Using Probabilistic Classifiers

Cited by 17 publications

References 13 publications

Skin Lesion Classification System Using Shearlets

Skin Lesion Classification System Using Shearlets

Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

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