Skin Cancer Classification Model Based on VGG 19 and Transfer Learning

Abuared, Nour; Panthakkan, Alavikunhu; Al-Saad, Mina; Amin, Saad; Mansoor, Wathiq

doi:10.1109/icspis51252.2020.9340143

Cited by 40 publications

(13 citation statements)

References 7 publications

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“…The core of any artificial intelligence-based algorithm lies in the collection of a high-quality dataset, which is subsequently employed for model training and testing. Using this trained model, the algorithms help to find different patterns and insights into those patterns [ [1] , [9] ].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“… Different methods for data validation are employed to convert the initial raw dataset into a refined version. These techniques include procedures such as eliminating noise, human-assisted labeling to establish ground truth, resizing images, as well as applying zoom and rotation transformations [1 , 7] . Factors such as image quality, severity of the illness, and demographic elements are being carefully considered.…”

Section: Value Of the Datamentioning

confidence: 99%

See 1 more Smart Citation

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people

Emu,

Niloy,

Karim

et al. 2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people

Emu,

Niloy,

Karim

et al. 2024

Data in Brief

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“…In the skin cancer classification model based on VGG-19 and transfer learning, model training and test accuracy were obtained by using the Human Against Machine dataset at a rate of 98.50% and 97.50%, respectively. Also, the model training and test loss were obtained as 0.009 and 0.119, respectively (Abuared et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Classification of skin cancer using VGGNet model structures

Kaya

Akgül

2022

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Skin cancer is one of the most common type of cancer in humans. This type of cancer is produced by skin cells called melanocytes and occurs as a result of division and multiplication of the mentioned cells. The most important symptom of skin cancer is the formation of spots on the skin or the observation of changes in the shape, color, or size of the existing spot. It is necessary to consult a specialist to distinguish the difference between a normal spot and skin cancer. Expert physicians examine and follow up the spots on the skin using skin surface microscopy, called dermatoscopy, or take a sample from the suspicious area and request it to be examined in laboratory environment. This situation increases the cost of the procedure for the diagnosis of skin cancer and also causes it to be treated at a later stage. Therefore, there is a need for a metod that can detect skin cancer early. Thanks to machine learning, become popular in recent years, many diseases can be diagnosed with software that helps expert physicians. In this study, VGGNet model structures (VGG-11, VGG-13, VGG-16, VGG-19) that quickly classify skin cancer and become a traditional convolutional neural network architecture using deep learning method, a subfield of machine learning, were used. It has been observed that the VGG-11 architecture, which is one of the VGGNet model structures, detects skin cancer with superior success accuracy (83%) compared to other model structures.

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“…DCNN model which was processed was not overly broad. Using additional information on images potentially improves network performance and robustness [10]. Recently proposed a method of extracting boundaries and identifying lesions via deep neural networks.…”

Section: Image Preprocessing and Lesion Segmentationmentioning

confidence: 99%

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

Li¹,

Pang²,

Meng³

et al. 2022

HSET

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Skin cancer, the most common cancer in the world, has many detection steps and the detection process is easy to make mistakes. A detection method based on convolutional neural network (CNN) is proposed to assist doctors in the detection. Based on the development of CNN in the classification and diagnosis of skin cancer in recent years, this paper compares and summarizes the development of each step in this process. After reviewing previous papers, it can be concluded that the classification process is roughly divided into four parts. In addition, the evaluation indicators of the model are further analyzed. AUC Sen and SPE are the most basic evaluation indicators in the model evaluation. As a skin classifier, CNN improves the accuracy of classification and diagnosis results to a great extent. CNN model has also made progress in "lightweight" and "concise". However, there are few evaluation indicators available for different CNN methods, and the evaluation latitude is relatively single. In the future, the evaluation indicators should develop in more aspects, it will enable to better understand the personality of a CNN model.

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Skin Cancer Classification Model Based on VGG 19 and Transfer Learning

Cited by 40 publications

References 7 publications

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people

Classification of skin cancer using VGGNet model structures

Convolution Neural Network Method for Skin Cancer Diagnosis: Comparison and Improvement

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