Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification Article Swipe
Irfan Ali Kandhro
,
Selvakumar Manickam
,
Kanwal Fatima
,
Mueen Uddin
,
Urooj Ali Malik
,
Anum Naz
,
Abdulhalim Dandoush
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e31488
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e31488
Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays a crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer detection. In this paper, we enhanced the VGG19 pre-trained model with max pooling and dense layer for the prediction of skin cancer. Moreover, we also explored the pre-trained models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual Network version 2 (InceptionResNetV2), Dense Convolutional Network 201 (DenseNet201), Residual Network 50 (ResNet50), Inception version 3 (InceptionV3), For training, skin lesions dataset is used with malignant and benign cases. The models extract features and divide skin lesions into two categories: malignant and benign. The features are then fed into machine learning methods, including Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM), our results demonstrate that combining E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. Moreover, we have also compared the performance of baseline classifiers and pre-trained models with metrics (recall, F1 score, precision, sensitivity, and accuracy). The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. This research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.heliyon.2024.e31488
- http://www.cell.com/article/S2405844024075194/pdf
- OA Status
- gold
- Cited By
- 53
- References
- 105
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397023773
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4397023773Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.heliyon.2024.e31488Digital Object Identifier
- Title
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Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-01Full publication date if available
- Authors
-
Irfan Ali Kandhro, Selvakumar Manickam, Kanwal Fatima, Mueen Uddin, Urooj Ali Malik, Anum Naz, Abdulhalim DandoushList of authors in order
- Landing page
-
https://doi.org/10.1016/j.heliyon.2024.e31488Publisher landing page
- PDF URL
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https://www.cell.com/article/S2405844024075194/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.cell.com/article/S2405844024075194/pdfDirect OA link when available
- Concepts
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Artificial intelligence, Support vector machine, Machine learning, Computer science, Pooling, Decision tree, Skin cancer, Deep learning, Residual, Convolutional neural network, Pattern recognition (psychology), Cancer, Medicine, Internal medicine, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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53Total citation count in OpenAlex
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2025: 37, 2024: 16Per-year citation counts (last 5 years)
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105Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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