Learning Hand-Crafted Features for K-NN based Skin Disease Classification

Kanca, Elif; Ayas, Selen

doi:10.1109/hora55278.2022.9799834

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…In one paper [75], the authors, after smoothing the images with a Gaussian filter, use the active contour model to obtain the lesion edges from which they define a segmentation mask to extract lesion characteristics in terms of shape. From the mask, they replace the lesion pixels with those of the original image and then extract lesion characteristics in terms of color and texture.…”

Section: Machine-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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Section: Machine-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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“…In one paper in 2022 [35], after smoothing pictures with a Gaussian filter, the authors applied an active contour model to find lesion borders and build a segmentation mask to extract lesion form attributes. They replaced the lesion pixels with those of the original picture and extracted colour and texture from the mask.…”

Section: K-nearest Neighbour Algorithmsmentioning

confidence: 99%

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

Yang,

Luo,

Greer

2024

Multimed Tools Appl

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Early detection of skin cancer from skin lesion images using visual inspection can be challenging. In recent years, research in applying deep learning models to assist in the diagnosis of skin cancer has achieved impressive results. State-of-the-art techniques have shown high accuracy, sensitivity and specificity compared with dermatologists. However, the analysis of dermoscopy images with deep learning models still faces several challenges, including image segmentation, noise filtering and image capture environment inconsistency. After making the introduction to the topic, this paper firstly presents the components of machine learning-based skin cancer diagnosis. It then presents the literature review on the current advance in machine learning approaches for skin cancer classification, which covers both the traditional machine learning approaches and deep learning approaches. The paper also presents the current challenges and future directions for skin cancer classification using machine learning approaches.

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“…The study in [10] used a K-nearest neighbor (KNN) model to classify melanoma and seborrhoeic nevi-keratoses into two distinct groups. The KNN model achieved an accuracy of 85%, demonstrating its potential for distinguishing between these two types of skin lesions.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancing Skin Cancer Prediction Using Ensemble Models

Natha,

RajaRajeswari

2024

Computers

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There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach.

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Learning Hand-Crafted Features for K-NN based Skin Disease Classification

Cited by 8 publications

References 12 publications

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Advancements in skin cancer classification: a review of machine learning techniques in clinical image analysis

Advancing Skin Cancer Prediction Using Ensemble Models

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