Melanoma Classification on Dermoscopy Skin Images using Bag Tree Ensemble Classifier

Lynn, Nay Chi; War, Nu

doi:10.1109/aitc.2019.8920908

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…Several researchers have evaluated their model on the PH2 dataset of dermoscopic images. Some of these researchers evaluated their model on the PH2 dataset, i.e., Adekanmi et al [37], Lynn et al [23], and Abbadi et al [22], and adopted a binary output classifier in their model. Adekanmi et al [37], Lynn et al [23], and Abbadi et al [22] achieved average accuracies of 95%, 84.5%, and 95.45%, respectively, but in binary output classifiers.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the recently evolved dermoscopic algorithms were developed to facilitate the ability to distinguish different types of melanocytic neoplasms, using the ABCD rule and low-level features [22][23][24][25]. However, recent studies suggest incorporation between high-and low-level descriptors is a sign that refers to lesion borders.…”

Section: Related Workmentioning

confidence: 99%

“…They evaluated the results on the PH2 dataset and achieved 95% accuracy and 92% dice coefficient. Lynn and War [23] demonstrated a detection system for lesion borders capable of extracting relevant features of dermoscopic structures for melanoma. They used a bagging decision tree ensemble classifier to classify features extracted using the ABCD rule.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned previously, this work aims to stage melanocytic neoplasms dermoscopic images. The stages of melanocytic neoplasms can be Melcyt NV, Dysp NV, or Mel [23]. The SVM and Gradient Boosted Trees (GBT) classifiers are trained using reduced high-low feature space to categorize the processed pixels into neoplasms stages.…”

Section: Classification Of Melanocytic Dermoscopic Imagesmentioning

confidence: 99%

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Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

et al. 2020

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The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Classification Of Melanocytic Dermoscopic Imagesmentioning

confidence: 99%

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Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

et al. 2020

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“…Ensembles of classifiers have shown to be more effective than a single classification algorithm in skin image classification problems [86,115,208]. Generally, the ensembles are created using the whole set of extracted features.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming based Feature Manipulation for Skin Cancer Image Classification

Ain¹

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<p>Skin image classification involves the development of computational methods for solving problems such as cancer detection in lesion images, and their use for biomedical research and clinical care. Such methods aim at extracting relevant information or knowledge from skin images that can significantly assist in the early detection of disease. Skin images are enormous, and come with various artifacts that hinder effective feature extraction leading to inaccurate classification. Feature selection and feature construction can significantly reduce the amount of data while improving classification performance by selecting prominent features and constructing high-level features. Existing approaches mostly rely on expert intervention and follow multiple stages for pre-processing, feature extraction, and classification, which decreases the reliability, and increases the computational complexity. Since good generalization accuracy is not always the primary objective, clinicians are also interested in analyzing specific features such as pigment network, streaks, and blobs responsible for developing the disease; interpretable methods are favored. In Evolutionary Computation, Genetic Programming (GP) can automatically evolve an interpretable model and address the curse of dimensionality (through feature selection and construction). GP has been successfully applied to many areas, but its potential for feature selection, feature construction, and classification in skin images has not been thoroughly investigated. The overall goal of this thesis is to develop a new GP approach to skin image classification by utilizing GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on utilizing a wide range of texture, color, frequency-based, local, and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively. This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach. This thesis develops a multi-tree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument. This thesis develops the first GP method utilizing frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods. This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations.</p>

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