The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer
Open accessAbstract
One of the most common types of human malignancies is skin cancer, which is chiefly diagnosed visually, initiating with a clinical screening followed by dermoscopic analysis, histopathological assessment, and a biopsy. Due to the fine-grained differences in the appearance of skin lesions, automated classification is quite challenging through images. To attain highly segregated and potentially general tasks against the finely grained object categorized, deep convolutional neural networks (CNNs) are used. In this paper, we propose a new prediction model that classifies skin lesions into benign or malignant lesions based on a novel regularizer technique. Hence, this is a binary classifier that discriminates between benign or malignant lesions. The proposed model achieved an average accuracy of 97.49%, which in turns showed its superiority over other state-of-the-art methods. The performance of CNN in terms of AUC-ROC with an embedded novel regularizer is tested on multiple use cases. The area under the curve (AUC) achieved for nevus against melanoma lesion, seborrheic keratosis versus basal cell carcinoma lesion, seborrheic keratosis versus melanoma lesion, solar lentigo versus melanoma lesion is 0.77, 0.93, 0.85, and 0.86, respectively. Our results showed that the proposed learning model outperformed the existing algorithm and can be used to assist medical practitioners in classifying various skin lesions.
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Cites
ImageNet classification with deep convolutional neural networks
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Dermatologist-level classification of skin cancer with deep neural networks
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The Balanced Accuracy and Its Posterior Distribution
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“Here, to tackle the imbalanced dataset, we used the weighted accuracy, which was defined as the average of the true positive rate obtained for each class [50].”
MethodAutomated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140
Proteomic applications for the early detection of cancer
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Breast cancer classification using deep belief networks
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Skin lesion classification with ensembles of deep convolutional neural networks
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Melanoma detection by analysis of clinical images using convolutional neural network
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Skin cancer detection: A review using deep learning techniques
Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105479 · Cited by 597
A systematic review of artificial intelligence techniques in cancer prediction and diagnosis
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09648-w · Cited by 223
Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001507 · Cited by 189
Melanoma detection using deep learning-based classifications
Alwakid, G., Gouda, W., Humayun, M., & Sama, N. U. (2022) · Healthcare · doi:10.3390/healthcare10122481 · Cited by 153
Blockchain Technology and Neural Networks for the Internet of Medical Things
Polap, D., Srivastava, G., Jolfaei, A., & Parizi, R. M. (2020) · IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162735 · Cited by 88
Metadata
- Authors
- Albahar, M. A.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 38306-38313
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2922703796
- Cited by
- 293
- References
- 29
- FWCI
- 10.34
- Type
- journal article
- Language
- en
- Open access
- gold
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