Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

Pitchiah, Maniraj Subramanian; Rajamanickam, Thamizhamuthu

doi:10.18280/ts.390524

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The minimum internal force is applied through the force of the balloon, which shows progress in the inner circumference of the lesion. Thus, the Euler-Lagrange expression defines the contour as the innermost descending as in Equation (5) [ 33 ].

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Section: Methodsmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

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…”

Section: Methodsmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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“…The area feature (A) quantifies the total number of pixels within the lesion area, while the perimeter feature (P) quantifies the total number of contour pixels on the boundary of the lesion area. Based on these two parameters, other shape descriptors, such as dispersion, saturation, and roundness, can be derived, as shown in ( 10)-( 12) [31]. In the shape characterization process, dispersion, saturation, and roundness were chosen as descriptors to fully present the shape characteristics of the lesion area.…”

Section: Shape Featurementioning

confidence: 99%

“…The best features were wrapped after repetition [4]. On the other hand, the importance and contribution of each feature were obtained for each elimination [31]. For this reason, the features could be ranked according to the size of the contribution.…”

Section: Feature Selection By Rfe Rankingmentioning

confidence: 99%

An Identification Method of Feature Interpretation for Melanoma Using Machine Learning

Li,

Ji,

Yang

et al. 2023

Applied Sciences

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Melanoma is a fatal skin cancer that can be treated efficiently with early detection. There is a pressing need for dependable computer-aided diagnosis (CAD) systems to address this concern effectively. In this work, a melanoma identification method with feature interpretation was designed. The method included preprocessing, feature extraction, feature ranking, and classification. Initially, image quality was improved through preprocessing and k-means segmentation was used to identify the lesion area. The texture, color, and shape features of this region were then extracted. These features were further refined through feature recursive elimination (RFE) to optimize them for the classifiers. The classifiers, including support vector machine (SVM) with four kernels, logistic regression (LR), and Gaussian naive Bayes (GaussianNB) were applied. Additionally, cross-validation and 100 randomized experiments were designed to guarantee the generalization of the model. The experiments generated explainable feature importance rankings, and importantly, the model demonstrated robust performance across diverse datasets.

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Pattern Recognition Based Skin Lesion Stage Analysis Using IoT

Kollu,

Sharma,

Kautish

et al. 2024

SN COMPUT. SCI.

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Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

Cited by 4 publications

References 20 publications

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

An Identification Method of Feature Interpretation for Melanoma Using Machine Learning

Pattern Recognition Based Skin Lesion Stage Analysis Using IoT

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