Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Yao, Peng; Shen, Shing Chuan; Xu, Mengjuan; Liu, Peng; Zhang, Fan; Xing, Jinyu; Shao, Pengfei; Kaffenberger, Benjamin H.; Xu, Ronald X.

doi:10.1109/tmi.2021.3136682

Cited by 115 publications

(39 citation statements)

References 58 publications

(88 reference statements)

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“…HAM1000 Dataset: The HAM10000 “Human Against Machine with 10,000 training images” dataset is one of the largest datasets, which contains 10,015 total dermoscopy images, used for detecting pigmented skin lesions, that are publicly accessible through the ISIC repository [ 32 ]. This dataset is grouped into seven different classes with a number of images, i.e., melanocytic nevus (nv = 6705), actinic keratosis (akiec = 327), dermatofibroma (df = 115), basal cell carcinoma (bcc = 514), vascular lesion (vacs = 115), benign keratosis (bkl = 1099), and melanoma (mel = 1113) [ 33 ]. The dataset contains 54% male and 45% female skin lesion images.…”

Section: Datasetsmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient.

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Section: Datasetsmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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“…Learnable gates were used in the model to show how the method used or combined features from various tasks. This strategy may be used to investigate how CNN models behave, potentially enhancing their clinical utility ( 64 ). proposed a deep convolutional network for skin lesion classification on the Derm7pt dataset.…”

Section: Dermatological Images and Datasetsmentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

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“…Moldovanu et al used an ensemble of machine-learning techniques instead of deep-learning methods [ 19 ]. Yao et al proposed a multi-weight new loss function to classify skin lesions on an imbalanced small dataset [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning

Wang

Xiao

et al. 2022

JPM

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A keloid results from abnormal wound healing, which has different blood perfusion and growth states among patients. Active monitoring and treatment of actively growing keloids at the initial stage can effectively inhibit keloid enlargement and has important medical and aesthetic implications. LSCI (laser speckle contrast imaging) has been developed to obtain the blood perfusion of the keloid and shows a high relationship with the severity and prognosis. However, the LSCI-based method requires manual annotation and evaluation of the keloid, which is time consuming. Although many studies have designed deep-learning networks for the detection and classification of skin lesions, there are still challenges to the assessment of keloid growth status, especially based on small samples. This retrospective study included 150 untreated keloid patients, intensity images, and blood perfusion images obtained from LSCI. A newly proposed workflow based on cascaded vision transformer architecture was proposed, reaching a dice coefficient value of 0.895 for keloid segmentation by 2% improvement, an error of 8.6 ± 5.4 perfusion units, and a relative error of 7.8% ± 6.6% for blood calculation, and an accuracy of 0.927 for growth state prediction by 1.4% improvement than baseline.

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Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Cited by 115 publications

References 58 publications

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Skin Cancer Classification With Deep Learning: A Systematic Review

A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning

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