Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions

Abbas, Qaisar

doi:10.14569/ijacsa.2017.080704

Cited by 4 publications

(4 citation statements)

References 13 publications

(15 reference statements)

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“…The Stanford team utilized the CNN-PA algorithm to train a network on their own dataset, which consisted of 129,450 images of skin lesions taken using mobile phones, as well as a subset of publicly available datasets. The accuracy of the network model reached 72.1% and 55.4% for the three-class classi cation task, which is comparable to the performance of 21 experts in differentiating basal cell carcinoma and malignant melanoma (12). In another investigation, a CNN was employed for extracting lesion features.…”

Section: Introductionmentioning

confidence: 71%

Diagnostic performance of machine-learning-based screening methods for skin disease

Zhang,

Jiang,

Qian

et al. 2024

Preprint

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The timely identification of skin diseases is of utmost importance, as it affects around 420 million individuals globally. In order to ensure prompt treatment, it is crucial to make a precise diagnosis. To aid in this process, machine learning (ML) techniques offer dependable screening approaches. Two researchers, operating independently, extensively investigated reputable studies sourced from PubMed, Web of Science, and Scopus databases. Their objective was to assess the efficacy of various ML methods and determine the suitability of a screening system for skin disease diagnosis. The analysis also considered factors such as parameters, dataset characteristics, and settings.

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Section: Introductionmentioning

confidence: 71%

Diagnostic performance of machine-learning-based screening methods for skin disease

Zhang,

Jiang,

Qian

et al. 2024

Preprint

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“…The accuracy performance of AlexNet, Xception, VGGNet16 and VGGNet19 architectures was reported as 84%, 90%, 89% and 90%, respectively [26]. Abbas et al [27] developed a deep learning (COE-Deep) architecture to detect melanocytic and non-melanocytic (MnM) skin lesions. They used the convolutional neural network (CNN) model for feature extraction and reported an average network test success of 90% sensitivity, 93% specificity, 91.5% accuracy, and AUC=0.92 in ISIC dataset [27].…”

Section: Related Workmentioning

confidence: 99%

“…Abbas et al [27] developed a deep learning (COE-Deep) architecture to detect melanocytic and non-melanocytic (MnM) skin lesions. They used the convolutional neural network (CNN) model for feature extraction and reported an average network test success of 90% sensitivity, 93% specificity, 91.5% accuracy, and AUC=0.92 in ISIC dataset [27]. Deif et al [28] proposed four different CNN architectures for the classification of skin lesions.…”

Section: Related Workmentioning

confidence: 99%

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

Taşar¹

2023

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Skin cancer has become one of the most common diseases due to the depletion of the ozone layer and the decrease in its protection. Detection and classification of skin cancer in the early stages of its development allows patients to receive appropriate treatment quickly. In this article, a modified CNN framework based on transfer learning is proposed for the classification of skin lesions from skin dermoscopy images. In the proposed framework, pretrained CNN architectures are used. VGG16, ResNet50, DeneNet121, MobileNet, and Xception models were pre-trained using ImageNet images and training weights. In the study training and tests were performed on the HAM10000 skin lesions data set. The classification accuracy of the modified DenseNet121, VGGNet16, ResNet50, MobileNet, and Xception models were calculated as 94.29%, 93.28%, 87.10%, 83.10%, and 80.05% respectively. It was observed that the accuracy success of the proposed transfer learning framework in skin lesion type classification surpasses classical deep learning architectures.

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“…Such results were obtained once they used a 10-fold cross-validation test. The proposed deep COE system is best suited to the classification of non-melanocytic skin lesions to improve accuracy, reliability, and accessibility of pigmented skin lesions screening system [15].…”

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confidence: 99%

International Journal of Electronics and Telecommunications

Mohamed

Zekry

2019

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Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.

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Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions

Abstract: These experimental results indicate that the proposed COE-Deep system is better than state-of-the-art systems. Hence, the COEDeep system is able to assist dermatologists during the screening process of skin cancer.

Cited by 4 publications

References 13 publications

Diagnostic performance of machine-learning-based screening methods for skin disease

Diagnostic performance of machine-learning-based screening methods for skin disease

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

International Journal of Electronics and Telecommunications

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