Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features

Ghahfarrokhi, Sepehr Salem; Khodadadi, Hamed; Ghadiri, Hamid; Fattahi, Fariba

doi:10.1016/j.bspc.2022.104300

Cited by 13 publications

(7 citation statements)

References 40 publications

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“…Combining the Radial Basis Function (RBF) kernel and Lagrange coefficients with SVM improves the separation of skin disease images by making a hyperplane for each classification. One other method is KNN, a non-parametric supervised classification approach that names things during the learning phase and sorts data points correctly during the testing phase by how close they are to their nearest neighbors [8]. Following the steps of Data Preparation, Feature Extraction, and Feature Vector Creation in another SVM work by Saranagata Kundu and colleagues, the dataset was used to train the SVM model.…”

Section: Support Vector Machine and K-nearest Neighbors Methodsmentioning

confidence: 99%

Innovating Skin Cancer Detection with Transfer Learning, Feature Fusion, Efficient Data Management, Data Augmentation and Interdisciplinary Expertise for Better Outcomes

Turanli,

CIM

2023

Preprint

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There is a growing acceptance of the use ofArtificial Intelligence (AI) in the use of healthcare,including in dermatology.AI is better at recognizingimages and learning patterns and due to this, i hasgrown into a powerful tool that can sometimes be moreaccurate than people and this has been demonstratedin recent studies showcasing the potential of AI.According to the World Cancer Research FundInternational, the global incidence of skin cancer maybe lesser than it is because of the challenges in datacollection, different types of skin cancer, interpretationof the results and demographics.Early detection and diagnosis in the majority of casessaves life. This has been proven overtime with mostNon-communicable diseases NCDs and this is nodifferent with skin cancer. This is where theapplication of AI in the diagnosis comes to play.In this study, we discussed the application of AI inMelanoma diagnosis. The use of machine learningmethods like Reinforcement Learning (RL), SupportVector Machines (SVM), K-Nearesr Neighbour (KNN)and Deep learning models like Convolutional NeuralNetwork (CNN), transfer learning and a combinationof both models were explored and showed significantaccuracies in Melanoma diagnosis compared to thetraditional methods. We also looked at the economicimpact of AI in healthcare, its cost-effectiveness andpublic perception of using AI.This study further emphasises the growing importanceof AI in healthcare and its integration into Melanomadiagnosis. It also explored the importance of AI inassisting dermatologists in the early detection of skincancer thereby saving lives.AI has the impact to makeour medical decision making easier, faster and withbetter accuracy and precision thereby increasingaccess to care.

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Section: Support Vector Machine and K-nearest Neighbors Methodsmentioning

confidence: 99%

Innovating Skin Cancer Detection with Transfer Learning, Feature Fusion, Efficient Data Management, Data Augmentation and Interdisciplinary Expertise for Better Outcomes

Turanli,

CIM

2023

Preprint

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“…Melanoma, regarded as the most lethal variant of skin cancer, represents a substantial proportion of mortality within the skin cancer category [ 159 ]. Notably, exosome-derived miRNAs have emerged as key contributors to the progression of melanoma, particularly by exerting a significant role in suppressing apoptosis [ 160 ].…”

Section: Exosomal Micrornas In Cancer Apoptosismentioning

confidence: 99%

Exosomal microRNAs in regulation of tumor cells resistance to apoptosis

Salehi,

Kamali,

Arab

et al. 2024

Biochemistry and Biophysics Reports

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“…Using PSO in IoT-based healthcare services presents challenges and limitations that warrant consideration. 99 One notable challenge is the sensitivity of PSO to parameter settings, where the optimal configuration may vary depending on the specific healthcare optimization problem. The tuning of parameters such as inertia weight and acceleration coefficients can be intricate and requires domain-specific knowledge.…”

Section: Pso Analysismentioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features

Cited by 13 publications

References 40 publications

Innovating Skin Cancer Detection with Transfer Learning, Feature Fusion, Efficient Data Management, Data Augmentation and Interdisciplinary Expertise for Better Outcomes

Innovating Skin Cancer Detection with Transfer Learning, Feature Fusion, Efficient Data Management, Data Augmentation and Interdisciplinary Expertise for Better Outcomes

Exosomal microRNAs in regulation of tumor cells resistance to apoptosis

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

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