Deep learning architecture using transfer learning for classification of skin lesions

Jasil, S. P. Godlin; Ulagamuthalvi, V.

doi:10.1007/s12652-021-03062-7

Cited by 26 publications

(12 citation statements)

References 15 publications

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“…A task is performed on the ISIC dataset having two classes with an accuracy of 0.957 [ 126 ]. The researchers utilized three models VGG16, VGG19, and Inception V3 using the ISIC and obtained accuracies of 77%, 76%, and 74%, respectively [ 127 ]. A methodology is defined that classifies samples of skin cancer with the help of ResNet, VGG19, and InceptionV3 on more than 24,000 samples.…”

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

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Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

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The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

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Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

“… Graphical Comparative Analysis of Classification Outcomes [ 126 , 127 , 128 , 130 , 131 , 132 , 133 , 134 , 135 , 137 , 138 , 139 ]. …”

Section: Figurementioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

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“…The lesion segmentation shows that DoubleU-Net outperforms U-Net. In [20] they used CNN network VGG19, Inception V3 and VGG16 which provides the best execution. These models provide the better execution of test data with an accuracy of Inception V3 74%, VGG16 77%, and VGG19 76%.…”

Section: Related Workmentioning

confidence: 99%

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition

Diame

ElBery

Salem³

et al. 2022

IJICIS

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Melanoma is a dangerous and metastatic cancer that may be fatal and it has a high ability to invade other tissues and organs. Early diagnosis is an important reason to recover from melanoma and reduce mortality. So, automatic skin segmentation is considered an enthusiastic study at present. In this paper, we investigate the applicability of deep learning approaches to the segmentation of skin lesions by evaluating five architectures: Deeplabv3plus, mobilenetv2_unet, Resnet50_unet, vgg19_unet by providing a comparative study of those methods. All methods were trained on the ISIC2017 dataset. The methods were trained on the original dataset, and then the dataset was pre-processed for use in training the five methods. We used quantitative evaluation metrics to evaluate the performance of the methods. The Deeplabv3+ architecture showed significant results compared to the rest of the architecture in F1 as high as 89%, Jaccard as high as 83% and Recall as high as 91%.

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“…For example, a model that was trained on classifying items like pencils, cars, etc., is used to classify skin lesions. 24 So far, TL has not been employed for rubber crack surfaces, as per the authors' knowledge. In this study, we aim to employ the concept of TL for surface feature−property analysis of viscoelastic silicone rubber.…”

Section: Introductionmentioning

confidence: 99%

Crack Surface Analysis of Elastomers Using Transfer Learning

Ghumman

Chen

D'Angelo

et al. 2023

ACS Appl. Mater. Interfaces

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Cracks that form during fatigue offer critical information regarding the fracture process of the associated material, such as the crack speed, energy dissipation, and material stiffness. Characterization of the surfaces formed after these cracks have propagated through the material can provide important information complementary to other in-depth analyses. However, because of the complex nature of these cracks, their characterization is difficult, and most of the established characterization techniques are inadequate. Recently, Machine Learning techniques are being applied to image-based material science problems in predicting structure−property relations. Convolutional neural networks (CNNs) have proven their capacity on modeling complex and diverse images. The downside of CNNs for supervised learning is that that they require large amounts of training data. One work-around is using a pre-trained model, i.e., transfer learning (TL). However, TL models cannot be used directly without modification. In this paper, to use TL for crack surface feature−property mapping, we propose to prune the pre-trained model to retain the weights of the first several convolutional layers. Those layers are then used to extract relevant underlying features from the microstructural images. Next, principal component analysis (PCA) is used to further reduce the feature dimension. Finally, the extracted crack features together with the temperature effect are correlated with the properties of interest using regression models. The proposed approach is first tested on artificial microstructures created by spectral density function reconstruction. It is then applied to experimental data of silicone rubbers. With the experimental data, two analyses are performed: (i) analysis of the correlation of the crack surface feature and material property and (ii) predictive model for property estimation, whereby the experiments can be potentially replaced altogether.

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Deep learning architecture using transfer learning for classification of skin lesions

Cited by 26 publications

References 15 publications

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Experimental Comparative Study on Autoencoder Performance for Aided Melanoma Skin Disease Recognition

Crack Surface Analysis of Elastomers Using Transfer Learning

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