Computer-aided Diagnosis of Skin Cancer: A Review

Razmjooy, Navid; Ashourian, Mohsen; Karimifard, Maryam; Estrela, Vania V.; Loschi, Hermes; Nascimento, Douglas; França, Reinaldo Padilha; Vishnevski, Mikhail

doi:10.2174/1573405616666200129095242

Cited by 103 publications

(32 citation statements)

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“…The combination of CAD and dermoscopy images can decrease unnecessary biopsies and help to early diagnosis of melanoma 6 . Image processing techniques, machine‐learning methods, and deep‐learning algorithms have been used for melanoma detection using CAD 7 . The lesions are segmented and diagnosed by manual feature extraction in classical machine learning methods 8 .…”

Section: Introductionmentioning

confidence: 99%

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Alizadeh

Mahloojifar

2020

Int J Imaging Syst Tech

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Melanoma is one of the most dangerous types of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this study, a system is proposed to detect melanoma automatically using an ensemble approach, including convolutional neural networks (CNNs) and image texture feature extraction. Two CNN models, a proposed network and the VGG‐19, were employed to classify images in the CNN phase. Furthermore, texture features were extracted, and their dimension was reduced using kernel principal component analysis (kPCA) to improve the classification performance in the feature extraction‐based phase. The results of each step were then combined to obtain the final diagnosis. The proposed method was evaluated on three databases, that is, ISIC 2016, ISIC 2019, and PH2. The accuracy, average precision, sensitivity, and specificity of the proposed method on the ISIC 2016 dataset were 85.2%, 66%, 52%, and 93.4%, respectively. These evaluation metrics for the ISIC 2019 database were obtained equal to 96.7%, 95.1%, 96.3%, and 97.1%, respectively. Furthermore, the accuracy, sensitivity, and specificity of the proposed method on the PH2 dataset were 97.5%, 100%, and 96.88%, respectively. According to the experimental results, the ensemble method improves the evaluation metrics compared to each phase separately. Besides, the proposed approach can increase the performance of melanoma detection, compared to previous studies.

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Section: Introductionmentioning

confidence: 99%

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Alizadeh

Mahloojifar

2020

Int J Imaging Syst Tech

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“…As performance metric was concerned they achieved 95% accuracy [23]. Navid Razmjooy et al discussed implantation capabilities based model for the automatic skin cancer detection [26]. As per the study of authors Ana Carolina Borges Monteiro et al, Software Defined Radio embedded with Wireless Body Area Network system proved beneficial for doctors to keep an eye on patient.…”

Section: Review Of Literaturementioning

confidence: 99%

Medical Data Classification using Fuzzy Main Max Neural Network Preceded by Feature Selection through Moth Flame Optimization

Dehariya¹,

Shukla²

2020

IJACSA

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“…Melanoma is considered one of the most malignant and deadly skin cancers worldwide. Although only about 1% of skin cancer is related to melanoma, it is the main reason of death among other skin cancer diseases [ 1 ]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of skin cancer based on soft computing techniques

et al. 2020

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Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

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Computer-aided Diagnosis of Skin Cancer: A Review

Cited by 103 publications

References 0 publications

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Medical Data Classification using Fuzzy Main Max Neural Network Preceded by Feature Selection through Moth Flame Optimization

Computer-aided diagnosis of skin cancer based on soft computing techniques

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