Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification

Majtner, Tomáš; Bajić, Buda; Yayilgan, Sule Yildirim; Hardeberg, Jon Yngve; Lindblad, Joakim; Sladoje, Nataša

doi:10.48550/arxiv.1808.05071

Cited by 7 publications

(11 citation statements)

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“…While at the same time, it is noticed from table that the proposed ensemble model has a higher F-Score, lower False-positive, and higher precision values of 92%, 16%, and 94% respectively as compared to individual learners. To add further, Table 1 also describes the performance of the proposed approach which is compared with the deep learning-based ensemble system developed in [15]. It can be notify from the table that the proposed ensemble performs better than the ensemble approach developed in [28] in terms of accuracy, sensitivity, and specificity.…”

Section: Resultsmentioning

confidence: 99%

Skin Cancer Detection Using Combined Decision of Deep Learners

et al. 2022

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Cancer is a deadly disease that arises due to the growth of uncontrollable body cells. Every year, a large number of people succumb to cancer and it's been labeled as the most serious public health snag. Cancer can develop in any part of the human anatomy, which may consist of trillions of cellules. One of the most frequent type of cancer is skin cancer which develops in the upper layer of the skin. Previously, machine learning techniques have been used for skin cancer detection using protein sequences and different kinds of imaging modalities. The drawback of the machine learning approaches is that they require humanengineered features, which is a very laborious and time-taking activity. Deep learning addressed this issue to some extent by providing the facility of automatic feature extraction. In this study, convolution-based deep neural networks have been used for skin cancer detection using ISIC public dataset. Cancer detection is a sensitive issue, which is prone to errors if not timely and accurately detected. The performance of the individual machine learning models to detect cancer is limited. The combined decision of individual learners is expected to be more accurate than the individual learners. The ensemble learning technique exploits the diversity of learners to yield a better decision. Thus, the prediction accuracy can be enhanced by combing the decision of individual learners for sensitive issues such as cancer detection. In this paper, an ensemble of deep learners has been developed using learners of VGG, CapsNet, and ResNet for skin cancer detection. The results show that the combined decision of deep learners is superior to the finding of individual learners in terms of sensitivity, accuracy, specificity, F-score, and precision. The experimental results of this study provide a compelling reason to be applied for other disease detection.

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Section: Resultsmentioning

confidence: 99%

Skin Cancer Detection Using Combined Decision of Deep Learners

et al. 2022

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“…Performance didn't improve much. Milton et al [52], Majtner et al [53] and Gessert et al [54] all employed ensemble models, but there are big differences in their performances. Milton [52] proposed the PNASNet-5-Large model.…”

Section: Resultsmentioning

confidence: 99%

“…Method Accuracy (%) [51] AdaBoost + random forest 73.08 [51] RGB+HSV+YIQ color model * 74.26 [52] Ensemble 73.00 [52] PNASNet * 76.00 [56] ResNet-50 + Forest 80.04 [53] VGG16 + GoogLeNet Ensemble 81.50 [54] Densenet121 with SVM 82.20 [54] Ensemble…”

Section: Sourcementioning

confidence: 99%

A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss

et al. 2022

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Skin cancers are the most cancers diagnosed worldwide, with an estimated > 1.5 million new cases in 2020. Use of computer-aided diagnosis (CAD) systems for early detection and classification of skin lesions helps reduce skin cancer mortality rates. Inspired by the success of the transformer network in natural language processing (NLP) and the deep convolutional neural network (DCNN) in computer vision, we propose an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images. First, the CNN extracts low-level, local feature maps from the dermoscopic images. In the second stage, the vision transformer (ViT) globally models these features, then extracts abstract and high-level semantic information, and finally sends this to the multi-layer perceptron (MLP) head for classification. Based on an evaluation of three different loss functions, the FL-based algorithm is aimed to improve the extreme class imbalance that exists in the International Skin Imaging Collaboration (ISIC) 2018 dataset. The experimental analysis demonstrates that impressive results of skin lesion classification are achieved by employing the hybrid model and FL strategy, which shows significantly high performance and outperforms the existing work.

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“…The CNN framework MobileNet proposed for the categorization of skin lesions using the HAM-10000 dataset in which accuracy is a little bit higher without data augmentation is 83.93% [ 138 ]. The GoogLleNet, VGG, and their ensemble were utilized for the categorization of seven classes utilizing the ISIC 2018 and the models are 79.7%, 80.1% and 81.5% accurate, respectively [ 139 ]. The summary of the existing classification approaches along with highest obtained results are mentioned in Table 3 .…”

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 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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Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification

Cited by 7 publications

References 0 publications

Skin Cancer Detection Using Combined Decision of Deep Learners

Skin Cancer Detection Using Combined Decision of Deep Learners

A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss

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

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