Deep Learning-Based Transfer Learning for Classification of Skin Cancer

Jain, Satin; Singhania, Udit; Tripathy, B. K.; Nasr, Emad Abouel; Aboudaif, Mohamed K.; Kamrani, Ali Κ.

doi:10.3390/s21238142

Cited by 64 publications

(29 citation statements)

References 46 publications

(40 reference statements)

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“…These features feed an ensemble of traditional classification algorithms, including support-vector machine (SVM), logistic label propagation (LLP), and k-nearest neighbors (KNN). Jain et al [30] compared six different transfer learning networks for multiclass lesion classification. However, their reported results relied upon increasing the size of the dataset by augmentation.…”

Section: Related Workmentioning

confidence: 99%

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Fraiwan

Faouri

2022

Sensors

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Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.

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Section: Related Workmentioning

confidence: 99%

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Fraiwan

Faouri

2022

Sensors

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“…The main reason that needs transfer learning is because malignant and benign lesions have high similarity, so it takes a long time to identify and classify them. Moreover, transfer learning is more efficient in classifying between similar lesions, making it a first choice [161]. These papers used transfer learning in the literature we surveyed [25], [26], [28], [30], [33]- [39], [41], [42], [46], [52], [58], [61], [62], [64], [66]- [68], [70]- [73], [75], [76], [76], [77], [85]- [87], [92], [102], [112], [113], [122], [124], [126], [127], [129], [141], [151], [152], [158], [159], [162]- [179].…”

Section: B Transfer Learningmentioning

confidence: 99%

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

et al. 2022

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Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learningbased solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. INDEX TERMSSkin cancer, Dermoscopy images, Deep learning, Classification, Literature review.FIGURE 1: Global heat map showing estimated agestandardized incidence rates, in 2020, of melanoma of the skin in all sexes, all ages. The map shows melanoma incidence in all parts of the world, except Greenland in the Arctic Circle. The regions most affected by skin melanoma globally are Europe, the United States, Canada, and Australia [2].microscopy-based tool to improve non-invasive diagnostic discrimination of skin lesions based on color and structure analysis [5]. This paper focuses on dermoscopy images. Because dermoscopic structures have direct histopathologic correlates, dermoscopic images help the dermatologist select management and treatment options for particular types of skin cancers [6]. In addition, dermoscopy can be useful for

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“…The models VGG16, Inception v3, MobileNets, ResNet, etc. An app based for early stage [28], CNN base model trained 129450 images. The result certified by 12 board of certified dermatologists.…”

Section: Literature Workmentioning

confidence: 99%

A combined approach of VGG 16 and LSTM transfer learning technique for skin melanoma classification

Arjun

Kumar

2022

ijhs

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To distinguish melanoma from other skin illnesses, doctors examine pigmented lesions on the skin. Damaged DNA causes cells to expand uncontrollably, and the rate of growth is currently increasing rapidly. Melanoma is a kind of skin cancer induced by UV radiation from the sun, with a survival rate of about 15-20 percent. Increased UV light on the earth's surface is also aiding the spread of skin cancer throughout the globe. Melanoma is diagnosed late, resulting in severe malignancy and spread to other bodily organs such as the liver, lungs, and brain. Melanoma diagnosis from dermoscopic skin samples automatically is a difficult problem. The purpose of Computer Vision, Machine Learning, and Deep Learning in the era of digital pictures is to extract information from them and develop new knowledge. Deep convolutional neural network (DCNN) models have been extensively studied for skin disease detection, with some achieving diagnostic results that are equivalent to or even better than dermatologists. Pre-processing involves first applying a filter or kernel to reduce noise and artefacts, then trained on a variety of tiny, unbalanced datasets to ensure that the moderately complicated models outperform the bigger models. Finally, to minimise overfitting, regularization DropOut is introduced.

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Deep Learning-Based Transfer Learning for Classification of Skin Cancer

Cited by 64 publications

References 46 publications

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

A combined approach of VGG 16 and LSTM transfer learning technique for skin melanoma classification

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