Skin Cancer Detection: A Review Using Deep Learning Techniques

Dildar, Mehwish; Akram, Shumaila; Irfan, Muhammad; Khan, Hikmat Ullah; Ramzan, Muhammad; Mahmood, Abdur Rehman; Alsaiari, Soliman Ayed; Saeed, Abdul Hakeem M; Alraddadi, Mohammed Olaythah; Mahnashi, Mater H.

doi:10.3390/ijerph18105479

Cited by 311 publications

(149 citation statements)

References 59 publications

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“…In a systematic survey of all the approaches used in skin lesion classification such as ANNs, CNNs, KNNs, and RBFNs, it was propounded that the right choice of algorithm is an important aspect to attain good classification efficiency. The survey reveals CNN provides a better skin cancer detection approach, and also, the acquisition phase of images plays a vital role in the performance of algorithms [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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“…Searches were focused on the following criteria: (a) topics as Me and NNs, (b) new trends (papers between 2017 and 2021), (c) the number of citations, (d) impact factors for journals, and (e) rate of ISI indexing for proceedings papers. We identified eight review or survey papers between 2018 and 2021 [134][135][136][137][138][139][140][141]. Table 5 highlights the characteristics of these articles and the differences of our article, marked as positive aspects or contributions.…”

Section: Discussionmentioning

confidence: 99%

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

Popescu

Elkhatib

El-Khatib

et al. 2022

Sensors

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Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018–2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.

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“…The various CNN based, KNN based, GAN with various datasets. Comparative analysis of 6 different TL algorithms [27] for multi class skin classification. The models VGG16, Inception v3, MobileNets, ResNet, etc.…”

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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Skin Cancer Detection: A Review Using Deep Learning Techniques

Cited by 311 publications

References 59 publications

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

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

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