Transfer learning for segmentation with hybrid classification to Detect Melanoma Skin Cancer

Dandu, Ravi; Murthy, M. Vinayaka; Kumar, Y.B. Ravi

doi:10.1016/j.heliyon.2023.e15416

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…For the purpose of detecting melanoma skin cancer, Dandu et al [75] introduced a unique method that combines transfer learning with hybrid classification. To increase the accuracy of melanoma detection, the authors developed a hybrid framework that uses pretrained deep learning models for segmentation and incorporates a hybrid classification technique.…”

Section: Machine Learning and Deep Learning In Skin Disease Detectionmentioning

confidence: 99%

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Debelee

2023

Diagnostics

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Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

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Section: Machine Learning and Deep Learning In Skin Disease Detectionmentioning

confidence: 99%

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Debelee

2023

Diagnostics

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“…In the research [1], the researchers paired the ensemble technique with transfer learning and hybrid classification to achieve high accuracy of 91% and lower error values. An auto-color correlogram filter, a binary pattern pyramid filter, a color layout filter, and correlated machine learning algorithms were all used in the methodology.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Melanoma Detection: Harnessing the Power of Machine Learning for Precise Skin Disease Diagnosis

Patinge,

Ponde,

Pokar

et al. 2023

Data Science and Intelligent Computing Techniques

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The most dreadful disease in the world is skin cancer. Australia is sometimes regarded as the nation most severely impacted by melanoma due to its high melanoma incidence rates. Though it is very rare in India, its incidence has been gradually increasing in recent years. While melanoma is relatively less common compared to other types of skin cancers in India, it is important to recognize that the disease can still occur. Due to melanoma's rapid development, early detection is vital for effective treatment and a greater chance of survival. By exploring melanoma detection, researchers desire to minimize the strain on healthcare systems along with the emotional toll it takes on individuals and their families. Early identification may significantly boost the probability of successful intervention, potentially saving people from invasive therapies and lengthy surgeries. The state of the art for melanoma detection using machine learning algorithms is presented in detail in this survey study. This study includes melanoma detection algorithms, data gathering techniques, pre-processing methods, feature extraction strategies, and classification systems. The architecture and criteria used to assess the performance of classification algorithms, including support vector machines, K-nearest neighbour, and Convolutional Neural Networks are covered. Moreover, this survey paper examines the limitations and open challenges in the field, such as time complexity, dataset biases, and generalizability of models. We conclude by identifying promising directions for future research, including addressing the class imbalance and enhancing the interpretability of machine learning models.

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A lightweight deep convolutional neural network model for skin cancer image classification

Tuncer,

Barua,

Tuncer

et al. 2024

Applied Soft Computing

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Transfer learning for segmentation with hybrid classification to Detect Melanoma Skin Cancer

Cited by 10 publications

References 34 publications

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Automated Melanoma Detection: Harnessing the Power of Machine Learning for Precise Skin Disease Diagnosis

A lightweight deep convolutional neural network model for skin cancer image classification

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