Review on Techniques and Steps of Computer Aided Skin Cancer Diagnosis

Mehta, Palak; Shah, Bhumika

doi:10.1016/j.procs.2016.05.238

Cited by 47 publications

(13 citation statements)

References 15 publications

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“…Sujaya et al used a graphical user interface to classify skin lesion [144]. whereas fuzzy C Mean was used by Palak et al for skin cancer analysis [145]. Sumithra et al used support vector machine for skin lesion classification [147].…”

Section: Discussionmentioning

confidence: 99%

“…In total, 27 different algorithms provided by 27 different researchers [42,65,67,68,70,74,86,141,142,143,144,145,147,148,149,150,151,152,153,155,156,157,158,159,161,167] are reviewed for skin cancer diagnosis. As discussed above, there are different methods with different algorithm schemes and different training datasets, which adds difficulty when comparing them.…”

Section: Discussionmentioning

confidence: 99%

“…Sujaya et al studied lesion probability using graphical user interface [144]. Palak et al proposed a method using Fuzzy C Mean (FCM) for skin cancer analysis [145]. Colored Unsupervised segmentation, k means clustering and Gradient Vector Flow (GVF) were used in [146].…”

Section: Models and Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir

Elahi

Ayub

et al. 2019

Cancers

321

182

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In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir

Elahi

Ayub

et al. 2019

Cancers

321

182

View full text Add to dashboard Cite

show abstract

“…During image acquisition, dermoscopic images having certain artifacts such as thin/thick hair, low contrast image resolution, dark spots/bubbles around the infected skin region and irregular lesion boundary that ultimately minimize accuracy of skin lesion detection. To handle these challenging tasks preprocessing help in accurate detection of the skin lesion [13]. The high pass filter is used to highlight the edges; further illumination is removed by homomorphic filter [14].…”

Section: Related Workmentioning

confidence: 99%

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

et al. 2020

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Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.

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“…In experimental settings, the performance of AI algorithms achieved accuracies that were on par or even exceeded the results obtained by experienced dermatologists [1][2][3][4][5][6]. Since early-stage detection of melanoma increases the chances of survival significantly [5], improved AI-based cancer diagnostics might reduce mortality as well as health care expenditure [9][10][11][12][13]. Consequently, an accurate distinction between skin cancer and noncancer through AI-based solutions is of great interest to support diagnosis [3,13,14].…”

Section: Introductionmentioning

confidence: 99%

Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

Haggenmüller¹,

Krieghoff-Henning²,

Jutzi³

et al. 2021

JMIR Mhealth Uhealth

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Background Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. Objective We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. Methods We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling. Results We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. Conclusions The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

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Review on Techniques and Steps of Computer Aided Skin Cancer Diagnosis

Cited by 47 publications

References 15 publications

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

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