Deep Neural Network for Melanoma Classification in Dermoscopic Images

Wang, Jiahao; Xingguang, Jin; Yuan, Weiqi; Zhi-Quan, Luo; Yu, Zhengyang

doi:10.1109/iccece51280.2021.9342158

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…By performing a search based on loss‐ensemble of four networks, they achieved 77% accuracy and 0.8430 AUC on complete ISIC 2019 dataset. Wang et al 36 presented a CNN‐based approach for melanoma classification, utilizing a network based on EfficientNet B5 architecture. They claimed that their approach captures more complex and fine‐grained feature representations for melanoma classification, which makes it superior to existing methods.…”

Section: Resultsmentioning

confidence: 99%

Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images

Soylemez

2023

Int J Imaging Syst Tech

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Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection‐based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size‐related various properties of dermoscopy images. To this end, EfficientNet models B3–B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross‐validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.

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Section: Resultsmentioning

confidence: 99%

Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images

Soylemez

2023

Int J Imaging Syst Tech

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“…Malignant skin lesion recognition from images can be framed as an image classification problem where conventional offthe-shelf pre-trained image embedding models can be directly applied. Jiahao et al [33] evaluated the applicability of VGG-16, ResNet-50, and EfficientNet-B5 on the ISIC 2020 dataset and found EfficientNet-B5 to yield the best AUC-ROC. Similarly, Zhang and Wang [34] found DenseNet-201 to perform the best when compared with VGG-16 and ResNet-50 on the ISIC 2020 dataset.…”

Section: A Skin Lesion Classification Using Only Imagesmentioning

confidence: 99%

DeepMetaForge: A Deep Vision-Transformer Metadata-Fusion Network for Automatic Skin Lesion Classification

Vachmanus,

Noraset,

Piyanonpong

et al. 2023

IEEE Access

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Skin cancer is a dangerous form of cancer that develops slowly in skin cells. Delays in diagnosing and treating these malignant skin conditions may have serious repercussions. Likewise, early skin cancer detection has been shown to improve treatment outcomes. This paper proposes DeepMetaForge, a deeplearning framework for skin cancer detection from metadata-accompanied images. The proposed framework utilizes BEiT, a vision transformer pre-trained as a masked image modeling task, as the image-encoding backbone. We further propose merging the encoded metadata with the derived visual characteristics while compressing the aggregate information simultaneously, simulating how photos with metadata are interpreted. The experiment results on four public datasets of dermoscopic and smartphone skin lesion images reveal that the best configuration of our proposed framework yields 87.1% macro-average F1 on average. The empirical scalability analysis further shows that the proposed framework can be implemented in a variety of machine-learning paradigms, including applications on low-resource devices and as services. The findings shed light on not only the possibility of implementing telemedicine solutions for skin cancer on a nationwide scale that could benefit those in need of quality healthcare but also open doors to many intelligent applications in medicine where images and metadata are collected together, such as disease detection from CT-scan images and patients' expression recognition from facial images.

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“…Wang jiahao et al established a n-etwork on the basis of Efficient-B5 to classify melanoma in dermoscopic images. Their network's advanced architecture allowed for accurate feature extraction and outperformed existing methods [19]. In this study, Subroto Singha et al conducted an analysis using the public ISIC 2020 database to categorize lesions.…”

Section: Related Workmentioning

confidence: 99%

The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach

Saeed,

Naseer,

Masood

et al. 2023

IEEE Access

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Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy.

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Deep Neural Network for Melanoma Classification in Dermoscopic Images

Cited by 15 publications

References 11 publications

Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images

Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images

DeepMetaForge: A Deep Vision-Transformer Metadata-Fusion Network for Automatic Skin Lesion Classification

The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach

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