Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Vaiyapuri, Thavavel; Balaji, Prasanalakshmi; Shridevi, S; Alaskar, Haya; Sbaï, Zohra

doi:10.1155/2022/2370190

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Choosing the best resource when there are several, frequently at-odds criteria accessible is a common "multicriteria decision-making (MCDM)" challenge. Therefore, the best alternative for a given input needs to be selected utilising a thorough and suitable process of material choosing [14]. The "COPRAS (COmplex PRoportional ASsessment) technique" is employed in this work to address several common material selection problems.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Comprehensive Performance of 5G Base Station using the COPRAS

2023

Data Analytics and Artificial Intelligence

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As wireless internet expands quickly, so does people's anticipation for the effectiveness and efficiency of mobile communication. After 2020, the fifth generation (5G) of cell communication technologies will take over as the primary direction for wireless technology growth. Problems like bad customer experience and inadequate coverage area commonly arise when the size of 5G ground stations progressively grows. Therefore, it is essential to assess the overall performance of 5G access points to identify any issues that may have arisen during base station installation. The "COPRAS (Complex Proportional Assessment)" technique is employed in this research to assess the overall performance of 5G network sites. In this study, specific 5G base stations in an area are chosen for a thorough performance assessment. Factors such as "Signal coverage area (104 m2), Annual average business loadings (kW), Per capita input cost (rupee/person), Electric field intensity (V/m), and User experience degree" are taken into consideration to assess the overall performance of a 5G ground station. The rank of alternatives using the COPRAS method for Base Station 1 is second, Base Station 2 is fifth, Base Station 3 is fourth, Base Station 4 is third, and Base Station 5 is first. The result from the COPRAS analysis shows that the base station with a signal coverage area of 26.7104 m2, annual average business loadings of 22 kW, and electric field intensity of 1.32 V/m is optimized as the best among the 5G base stations considered.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Comprehensive Performance of 5G Base Station using the COPRAS

2023

Data Analytics and Artificial Intelligence

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“…This method offered an accuracy rate of 100% for the detection of melanoma, a sensitivity rate of 94.05%, and a precision rate of 97.07%. Vaiyapuri et al developed a novel computational intelligence-based melanoma detection and classification approach using dermoscopic images with maximum accuracy of 97.50% [21]. Arshad et al similarly used the HAM10000 database to apply an automated framework for multiclass skin lesion classification, obtaining an accuracy rate of 91.7% [25].…”

Section: Artificial Intelligence-based Approaches Applied To Dermosco...mentioning

confidence: 99%

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review

Patel,

Foltz,

Witkowski

et al. 2023

Cancers

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Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.

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“…Figure 2 shows the comparison of dermoscopic images before and after processing, and the abovementioned problems such as noise were improved. The k-means clustering algorithm is an unsupervised clustering technique based on partitioning, which is known for its fast convergence and easy implementation [21]. The k-means clustering algorithm is known to provide locally optimal solutions [22].…”

Section: Preprocessing Of Dermoscopic Imagesmentioning

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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Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Cited by 6 publications

References 26 publications

Assessment of the Comprehensive Performance of 5G Base Station using the COPRAS

Assessment of the Comprehensive Performance of 5G Base Station using the COPRAS

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review

An Identification Method of Feature Interpretation for Melanoma Using Machine Learning

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