Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks

Hosny, Khalid M.; Kassem, Mohamed A.; Foaud, Mohamed M.

doi:10.1007/s11042-020-09067-2

Cited by 63 publications

(37 citation statements)

References 32 publications

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“…Öztürk et al [194] proposed a segmentation system for skin lesions. Hosny et al [195] proposed a new deep CNN classification system for skin lesions. The authors performed three different experiments with three datasets.…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Section: Deep Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…Also, up to 73% of the reviewed skin lesion classification methods were based on the deep learning algorithm (KNN), where these models based on transfer learning like GoogleNet and Inception-v3 in [72], DenseNet 201 in [73], AlexNet in [79,80] Inception v3, InceptionResNet-v2, and ResNet 152 in [84], fusion MobileNet and DenseNet in [85], DenseNet in [8]. The best accuracy was obtained among them in [86] GoogelNet 99.29%, in [79] AlexNet 98.61%, and the DenseNet area under curve AUC is 98.16%. While other reviewed skin lesion classification methods were based on Optimized neutrosophic k-means (ONKM) in [74] Genetic algorithm for optimizing the value of α in α-mean operation in the neutrosophic set.…”

Section: Discussionmentioning

confidence: 99%

“…A new deep convolutional method of classification based on neural networks is proposed in [86]. The approach proposed involves three key measures.…”

Section: Fig 9 Densenet Architecturementioning

confidence: 99%

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

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Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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“…In recent years, convolutional neural networks play an important role in skin lesion classification [20]- [23]. This innovative application has achieved excellent classification performance over conventional intelligent methods [24]- [26].…”

Section: A Supervised Methods In Skin Lesion Classificationmentioning

confidence: 99%

“…This innovative application has achieved excellent classification performance over conventional intelligent methods [24]- [26]. Esteva et al [24] demonstrated classification of skin lesions using a single convolutional neural network (CNN), trained end-to-end from images directly, using only pixels and disease labels as inputs, and it achieved performance on identification of the deadliest skin cancer and most common cancers; Gessert et al [25] proposed a patch-based attention architecture that provides global context between small and high-resolution patches with a novel diagnosis guided loss weighting method, outperformed previous methods and improves the mean sensitivity by 7%; Harangi et al [26] designed a deep convolutional neural network framework to classify dermoscopy images into seven classes, using GoogLeNet Inception-v3 and achieving remarkable improvement of 7%; Hosny et al [23] ing obstacles for the model's expansibility and efficiency, because it is necessary to convene professional doctors with costing much time to generate massive annotations. For the sake of relaxing this inconvenience, many exploitations [13], [14], [27]- [30] have been investigated to apply transfer learning into skin lesion classification.…”

Section: A Supervised Methods In Skin Lesion Classificationmentioning

confidence: 99%

Skin Lesion Classification by Multi-View Filtered Transfer Learning

et al. 2021

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Skin cancer is one of the most deadly cancer types with considerable number of patients.Image analysis has largely improved the automated diagnosis accuracy for malignant melanoma and other pigmented skin lesions, compared to unaided visual examination. Recent popular solution for automated skin lesion classification is using deep neural networks, trained from large amounts of professional annotated data, but that largely limits the model's scalability. This paper exploits transfer learning for skin lesion classification task with the help of labeled data from another domain (source), and proposes a multi-view filtered transfer learning network to strongly represent discriminative information from different image views with reasonable weighing strategy. This method also evaluates the importance for each source images, which can learn useful knowledge with neglecting negative samples from source domain. The extensive skin lesion classification experiments demonstrate our method can effectively solve Melanoma and Seborrheic Keratosis classification tasks with outstanding extensibility, and the discussion of the major components also testifies the improvements of our proposed multi-view filtered transfer learning approach.

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Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks

Cited by 63 publications

References 32 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Skin Lesions Classification Using Deep Learning Techniques: Review

Skin Lesion Classification by Multi-View Filtered Transfer Learning

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