Skin lesion detection using an ensemble of deep models: SLDED

Shahsavari, Ali; Khatibi, Toktam; Ranjbari, Sima

doi:10.1007/s11042-022-13666-6

Cited by 13 publications

(7 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in [87], an ensemble learning approach is used. An ensemble of deep model (SLDEP) is created by using four different CNNs (GoogLeNet, VGGNet, ResNet, and ResNeXt) to perform multiclass classification based on majority voting.…”

Section: Deep-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

show abstract

Section: Deep-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In several other studies, ensemble learning has been utilized for skin lesion classification tasks. One approach involves combining the predictions of multiple CNNs (GoogLeNet, VGGNet, ResNet, and ResNeXt) through majority voting [32]. Another approach involves transfer learning (TL) from pre-trained networks and adding pooling and fully connected (FC) layers [33].…”

Section: Related Workmentioning

confidence: 99%

Deep learning model enhanced skin cancer detection

Kolachina

Agada²,

Li³

et al. 2023

Biophotonics and Immune Responses XVIII

View full text Add to dashboard Cite

According to the American Academy of Dermatology Association, identifying the types of skin cancer depends on the origin of a cell mutation resulting in the rapid growth of these abnormal cells in the epidermis. These mutations lead the skin cells to multiply rapidly and form malignant tumors. Skin cancer is ranked as the 17th most common form of cancer worldwide according to the World Cancer Research Fund. Skin cancer treatments cost the United States more than $8 billion (about $25 per person in the US) each year, making skin cancer the fifth most costly cancer for Medicare. Furthermore, skin cancer is an under recognized problem for diverse populations, including young women and minorities. Researchers have been exploring different technologies to detect skin care at its early stage to avoid high mortality rate and expensive medical treatment. This paper presents a novel ensembled deep learning model for the early detection of skin cancer. Our research is based on The HAM10000 dataset, a diverse collection of multi-sourced dermatoscopic images of common pigmented skin lesions which consists of 10015 dermatoscopic images. We have compared several deep learning neural network architectures and classifiers such as DNN, RNN, SVM and KNN in terms of accuracy rate and computation complexity and presented an ensembled deep learning model for early skin cancer detection. The main contribution of this paper is the productions of a comparative study of several skin cancer detection techniques using powerful computer vision techniques and deep learning models and a novel ensembled deep learning model for skin cancer detection.

show abstract

“…However, when the segmentation fails to detect ROI, it can affect the classi cation results. On the other hand, the segmentation process requires longer time [20].…”

Section: Introductionmentioning

confidence: 99%

WELDONNet: Weighted Ensemble DenseNet121, MobileNetV2, and NASNetMobile to improve skin lesion diseases classification

Sutaji

Yıldız

2023

Preprint

View full text Add to dashboard Cite

Skin cancer is classified as an aggressive type of cancer spreading quickly to other organs and tissues. Thus, inappropriate detection of the condition might lead to mortality. Melanoma is the most lethal variety, as it can extend to all human body parts. Early detection by dermatologists is, however, challenging in terms of consistency and timing. Hence, CAD systems have been utilized. Yet, the problems often encountered include class imbalance and noise. Therefore, the present authors propose a new method for detecting multi-class skin lesions by employing weighted ensemble convolutional neural networks (CNN). This study consisted of several steps, firstly, up-sampling and down-sampling of datasets were implemented to overcome the class imbalance problem, and image resizing was performed to scale down the image pixels in order to decrease noise. Secondly, the 20-layered and pre-trained CNN models were introduced. Particularly in the 20-layered model, the three fully connected layers were evaluated before the classification layer. Further, the last block layers were applied for the pre-trained model to obtain more specific features from the skin lesion images. Lastly, the weighted ensemble approach was conducted to improve classification performance. The observed results on the HAM10000 dataset indicate that the proposed strategy has improved an accuracy by 0.43% and 2.99% for 20-layered and pre-trained CNNs, respectively. Furthermore, the proposed WELDONNet model outperformed other CNNs with an accuracy of 99.36%. In sum, the proposed model was applicable to be implemented as a reference for skin cancer early detection systems as well as for prospective further research.

show abstract

Skin lesion detection using an ensemble of deep models: SLDED

Cited by 13 publications

References 57 publications

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Deep learning model enhanced skin cancer detection

WELDONNet: Weighted Ensemble DenseNet121, MobileNetV2, and NASNetMobile to improve skin lesion diseases classification

Contact Info

Product

Resources

About