Soft-Attention Improves Skin Cancer Classification Performance

Datta, Soumyya Kanti; Shaikh, Mohammad Abuzar; Srihari, Sargur N.; Gao, Mingchen

doi:10.1101/2021.05.12.21257114

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Although we obtained a lower value of 0.034 for the sensitivity metric than Ding et al who achieved tremendous success, we had a higher value of 0.012 for the AUC metric than the relevant study 34 . For the AUC score, our method obtains 0.923, which is 0.036 lower than the best score 35 . Even though our success metrics are slightly lower than those of the most recent studies, our results appear to be significantly superior to ensemble learning studies and other artificial intelligence methodologies.…”

Section: Discussioncontrasting

confidence: 69%

“…34 For the AUC score, our method obtains 0.923, which is 0.036 lower than the best score. 35 Even though our success metrics are slightly lower than those of the most recent studies, our results appear to be significantly superior to ensemble learning studies and other artificial intelligence methodologies. Our success rate appears to be more than 10% higher than the support vector classifier and KNN techniques.…”

Section: Discussioncontrasting

confidence: 62%

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Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Duman

Tolan

2023

Int J Imaging Syst Tech

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If you decided to utilize deep learning in any image processing application, you would be faced with the issue, "Which architecture should I use?" due to the proliferation of existing CNN models and their advancements. Unfortunately, your answer will only be partially correct because each alternative has its advantage. The underlying idea of this research is to combine recent CNN models instead of selecting just one for optimal accuracy. Our study applied this idea to color lesion images to diagnose skin diseases. By ensembling, the recent CNNs, over 99% classification accuracy and over 97% sensitivity were achieved for the ISIC-2017 dataset, which contains 2000 lesion images. Our mean sensitivity and AUC values for classifying 10000 color lesion images into seven different skin diseases (ISIC-2018) were 0.825% and 0.922%, respectively.In categorizing over 25000 images from the ISIC 2019 dataset, our suggested technique achieved a mean sensitivity of over 90%.

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Section: Discussioncontrasting

confidence: 69%

Section: Discussioncontrasting

confidence: 62%

Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Duman

Tolan

2023

Int J Imaging Syst Tech

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“…Inception ResNetV2 is a pretrained model on million images of ImageNet dataset. To focus more on the skin lesion area, here attention 45 is applied with the Inception ResNetV2 model. A few portions of the input data are enhanced while others are reduced.…”

Section: Methodsmentioning

confidence: 99%

Multiclass skin cancer classification using particle swarm optimization and convolutional neural network with information security

Shaheen

Singh

2022

J. Electron. Imag.

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Skin cancer is one of the world's most common cancers. Among different skin cancer types, melanoma is very dangerous. Its treatment is possible if detected at an early stage. Detection of skin cancer is still challenging, even for experienced dermatologists, due to the high similarity between benign and malignant lesions. Deep learning technologies help in diagnosing skin lesions and classifying them. They are used to detect lesions at an early stage. However, it still struggles with complex skin lesions because of complicated properties such as fuzzy boundaries, artifacts, low background contrast, and insufficient training datasets. Medical images require information security also. This paper proposes a method using particle swarm optimization (PSO) with the ensemble of pretrained convolutional neural network PSOCNN architecture for skin disease classification with information security. To evaluate the proposed architecture, 10,015 samples of the publicly available HAM1000 (human against machine) dataset comprise seven distinct classes. It is the case of a multiclass skin lesion classification problem. This method achieves 97.82% accuracy and a macroaverage ROC curve area of 0.99. The average F1 score obtained is 0.98. This work has been compared with different studies present in the literature. The result shows that this work has achieved comparable classification accuracy. It helps in achieving better performance for all classes of multi-class classification.

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“…In May 2021, Soumyya Kanti Datta and colleagues [20] proposed a method of applying soft-attention to improve performance in skin lesion classification. They found that in skin lesions images, only a small fraction of the pixels are actually involved, while the rest of the image is usually normal skin along with confounding objects such as hairs and blood vessels.…”

Section: Soft-attention Improves Skin Cancer Classification Performancementioning

confidence: 99%

MetaAttention model: a new approach for skin lesion diagnosis using AB features and attention mechanism

Nguyen

et al. 2023

Biomed. Phys. Eng. Express

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Ozone depletion has always been a hot crisis around the globe. Its consequence is the increase in ultraviolet radiation at the surface in many regions and countries, which then causes danger to the human immune system, eyes, and especially skin - the part that is directly exposed most to the sunlight. According to the World Health Organization, the number of cases of skin cancer is higher than that of breast, prostate, and lung cancer combined. Therefore, there has been a lot of research to apply deep learning models in solving the problem of skin cancer classification. This paper proposes a novel approach, named MetaAttention, aimed at improving the performance of transfer learning models for skin lesion classification. The method combines image features with patients’ metadata features using the attention mechanism, incorporating clinical knowledge related to ABCD signals to better distinguish melanoma cell carcinoma disease, which has long been a significant challenge for researchers. The experimental results indicate that the proposed approach outperforms the stateof-the-art method, EfficientNet-B4, achieving an accuracy of 89.9% with Scale-dot product MetaAttention and 90.63% with Additive MetaAttention. The method has the potential to support dermatologists in diagnosing skin lesions effectively and efficiently. Furthermore, with larger datasets, our method could be further fine-tuned to achieve even better performance on a broader range of labels.

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Soft-Attention Improves Skin Cancer Classification Performance

Cited by 8 publications

References 22 publications

Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis

Multiclass skin cancer classification using particle swarm optimization and convolutional neural network with information security

MetaAttention model: a new approach for skin lesion diagnosis using AB features and attention mechanism

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