Skin cancer detection and classification using machine learning

Monika, M. Krishna; Vignesh, N. Arun; Kumari, Ch. Usha; Kumar, Manoj; Lydia, E. Laxmi

doi:10.1016/j.matpr.2020.07.366

Cited by 76 publications

(28 citation statements)

References 6 publications

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“…• Monika et. Al [17], This paper discusses an approach based on the MSVM classification, where it uses two effective methods called ABCD and MSVM for feature extraction. The accuracy achieved is 96.25%.…”

Section: • Garg Et Al [9]mentioning

confidence: 99%

Machine Learning Techniques for the Skin Cancer Detection: A Review

2022

IRJMETS

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Skin cancer is the most common cancer worldwide, and occurrence rates are continuing to increase. The prediction of the skin cancer is essential to eliminate it on time, so that the life of patient may be saved. The early-stage detection of such disease is helpful for increasing the life span of suffering patient. In addition, it also reduces the cost of treatment. It is better to build an precise expert system, which helps to detect these diseases before leading to terrible conditions. Therefore, many lives can be saved by using these intuitive systems. This paper provides the concise review of medical expert systems and presents the best methods used to recognize the skin cancer.

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Section: • Garg Et Al [9]mentioning

confidence: 99%

Machine Learning Techniques for the Skin Cancer Detection: A Review

2022

IRJMETS

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“…The former methods are based on traditional machine learning which includes image preprocessing, image segmentation, and feature extraction that mine low-level radiomics features based on handcrafted approaches. Monica et al [ 35 ] proposed an automated system based on low-level radiomics feature extraction methods such as grey level covariance matrix (GLCM) and some statistical features to learn an SVM classifier to classify 8 subclasses of SC reaching an accuracy of 96.25%. Likewise, Arora et al [ 36 ] fused several low-level features using bag of features (BoF) with SURF features to classify skin images into cancerous and noncancerous.…”

Section: Background On Artificial Intelligence In Skin Cancer Diagnosismentioning

confidence: 99%

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Attallah

Sharkas

2021

Contrast Media & Molecular Imaging

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The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

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“…Many deep learning architectures appeared [9], such as LeNet architecture [10], [11], which used the detection of benign and malignant tumors. AlexNet architecture appeared [12], [13], which distinguished multiple types of skin diseases and developed the ZFNet algorithm to deal with different types of diseases [14], [15].…”

Section: Introductionmentioning

confidence: 99%

A proposed architecture for convolutional neural networks to detect skin cancers

Ahmed

Kashmola

2022

IJ-AI

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The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.

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Skin cancer detection and classification using machine learning

Cited by 76 publications

References 6 publications

Machine Learning Techniques for the Skin Cancer Detection: A Review

Machine Learning Techniques for the Skin Cancer Detection: A Review

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

A proposed architecture for convolutional neural networks to detect skin cancers

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