Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem, Omar Sedqi; Abdulazeez, Adnan Mohsin; Zeebaree, Diyar Qader

doi:10.9734/ajrcos/2021/v9i130210

Cited by 9 publications

(6 citation statements)

References 79 publications

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“…"Support vector machine" (SVM) showed 90% accuracy, which is much lower than the accuracy of CNN. On the contrary, TR has observed 98.7% accuracy of SVM during skin cancer detection [19]. erefore, confusion arises between these two studies.…”

Section: Literature Reviewmentioning

confidence: 98%

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

C.¹,

G²,

Sajitha³

et al. 2022

Journal of Food Quality

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Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and p values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.

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Section: Literature Reviewmentioning

confidence: 98%

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

C.¹,

G²,

Sajitha³

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…Extensive systems use images of different birthmarks, each of the diseases is marked as a class. The review of the multiclass classification was carried out in many works [2,4,21,36,38,47]. Table 1 presents a list of works that achieved a high ACC score for the classification of skin birthmarks based on CNN.…”

Section: Lesions Classification Processmentioning

confidence: 99%

“…Dermatoscopy is a non-invasive imaging technique that allows you to make many, high-resolution images of skin birthmarks. Diagnostics based on images is currently very effective due to the use of algorithms based on artificial intelligence [7,21,24,26]. The skin nevus diagnosed by the algorithm to confirm the diagnosis should be examined by a doctor.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Skin Less Ions Classification Based on Deep Neural Networks

Michalska

2022

IAPGOS

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Skin diseases diagnosed with dermatoscopy are becoming more and more common. The use of computerized diagnostic systems becomes extremely effective. Non-invasive methods of diagnostics, such as deep neural networks, are an increasingly common tool studied by scientists. The article presents an overview of selected main issues related to the multi-class classification process: the stage of database selection, initial image processing, selection of the learning data set, classification tools, network training stage and obtaining final results. The described actions were implemented using available deep neural networks. The article pay attention to the final results of available models, such as effectiveness, specificity, classification accuracy for different numbers of classes and available data sets.

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“…There are two phases: training and testing. A model is built during the training process that maps the extracted characteristics on labels [15]. In the test process, the model is used to evaluate unlabeled data classes.…”

Section: Supervised Learningmentioning

confidence: 99%

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Kareem

Al-Sulaifanie

Hasan

et al. 2021

AJRCoS

Self Cite

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A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

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Skin Lesions Classification Using Deep Learning Techniques: Review

Cited by 9 publications

References 79 publications

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

Multiclass Skin Less Ions Classification Based on Deep Neural Networks

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

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