DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

Zafar, Mehwish; Amin, Javeria; Sharif, Muhammad; Anjum, Muhammad Almas; Mallah, Ghulam Ali; Kadry, Seifedine

doi:10.3390/math11020364

Cited by 22 publications

(9 citation statements)

References 83 publications

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“…Two models closest to our precision performance were in accordance with [39] and [38], with 99.10% and 99.17%, respectively. Likewise, there were two closest models for the f1-score performance, such as in [40], and [41], with 98.60% and 97.24%, respectively. This comparison is detailed in Table 5.…”

Section: Performance Comparison Against Sotamentioning

confidence: 93%

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

Sutaji

Yıldız

2023

Preprint

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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.

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Section: Performance Comparison Against Sotamentioning

confidence: 93%

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

Sutaji

Yıldız

2023

Preprint

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“…Budhiman et al [39] 87 (for normal and melanoma class) Bibi et al [32] 85.4 Mahbod et al [40] 86.2 Ali et al [41] 87.9 Carcagnì et al [42] 88 Sevli [43] 91.51 Mehwish et al [44] 92.01 Bansal et al [45] 94.9 (for normal and melanoma class)…”

Section: Study Accuracy (%)mentioning

confidence: 99%

“…In contrast, in [32], the deep features were extracted using the DensNet-201 and DarkNet-53, and a marine predator optimizer was applied to extract the useful features, an approach that yielded an accuracy of 85.4% for seven class ISIC2018 datasets. Furthermore, Mehwish et al [44] used a wrapper-based approach to remove the redundant deep features and reported a high accuracy of 92.01%. However, the feature selection approach with CNN can enhance the complexity and compatibility issues with dependencies on the external algorithms.…”

Section: Study Accuracy (%)mentioning

confidence: 99%

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

Ali,

Khalid,

Alshanbari

et al. 2023

Bioengineering

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The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed–trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

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“…In [25][26][27][28][29][30][31][32][33][34][35], it was observed that the presence of irrelevant or redundant features may lead to the incorrect prediction of images and may bring down the classification results. Many techniques [25][26][27][28][29][30][31][32][33][34][35] employ feature selection (FS) algorithms for selecting the most appropriate features in order to maximize the classification performance.…”

Section: Related Workmentioning

confidence: 99%

Blend of Deep Features and Binary Tree Growth Algorithm for Skin Lesion Classification

Kumar,

Nath,

Hazarika

2023

Symmetry

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One of the most frequently identified cancers globally is skin cancer (SC). The computeraided categorization of numerous skin lesions via dermoscopic images is still a complicated problem. Early recognition is crucial since it considerably increases the survival chances. In this study, we introduce an approach for skin lesion categorization where, at first, a powerful hybrid deep-feature set is constructed, and then a binary tree growth (BTG)-based optimization procedure is implemented using a support vector machine (SVM) classifier with an intention to compute the categorizing error and build symmetry between categories, for selecting the most significant features which are finally fed to a multi-class SVM for classification. The hybrid deep-feature set is constructed by utilizing two pre-trained models, i.e., Densenet-201, and Inception-v3, that are fine-tuned on skin lesion data. These two deep-feature models have distinct architectures that characterize dissimilar feature abstraction strengths. This effective deep feature framework has been tested on two publicly available challenging datasets, i.e., ISIC2018 and ISIC2019. The proposed framework outperforms many existing approaches and achieves notable {accuracy, sensitivity, precision, specificity} values of {98.50%, 96.60%, 97.84%, 99.59%} and {96.60%, 94.21%, 96.38%, 99.39%} for the ISIC2018 and ISIC2019 datasets, respectively. The proposed implementation of the BTG-based optimization algorithm performs significantly better on the proposed feature blend for skin lesion classification.

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DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

Cited by 22 publications

References 83 publications

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

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

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

Blend of Deep Features and Binary Tree Growth Algorithm for Skin Lesion Classification

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