BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification

Ajmal, Muhammad; Khan, Muhammad Attique; Akram, Tallha; Alqahtani, Abdullah; Alhaisoni, Majed; Armghan, Ammar; Althubiti, Sara A.; Alenezi, Fayadh

doi:10.1007/s00521-022-08084-6

Cited by 15 publications

(4 citation statements)

References 55 publications

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“…Specifically, compared to the extended hybrid model + handcrafted feature model by Sharafudeen et al (2023) [38], our SkinSwinViT model significantly enhances predictive accuracy, precision, and specificity while maintaining a more parsimonious architecture, with improvements of 5.9%, 3.7%, and 1.6%, respectively. Furthermore, compared to the outstanding BF 2 SkNet model by Ajmal et al (2023) [43], our SkinSwinViT model demonstrates exceptional performance, with increases in accuracy and precision of 0.7% and 2.7%, respectively, under similar complexity conditions. These notable outcomes establish our SkinSwinViT model as the state-of-the-art method in the field of skin lesion classification.…”

Section: Discussionmentioning

confidence: 93%

“…MobileNet+handcrafted features [36] 92.4 92.1 90.0 ResNet+Inceptionv3 [37] 85.1 79.6 82.91 Hybrid Model+handcrafted features [38] 91.9 94.1 97.7 Deep learning and moth flame optimization [39] 90.6 ----A CNN-based pigmented framework [40] 91.5 ----A CNN and nature-inspired optimization algorithm [41] 91.7 92.4 --Two-stream CNN framework [42] 96.5 ----BF 2 SkNet model [43] 97.1 95.1 --Proposed SkinSwinViT 97.8 97.8 99.3…”

Section: Techniquementioning

confidence: 99%

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SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

Tang,

Su,

Chen

et al. 2024

Applied Sciences

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In recent decades, skin cancer has emerged as a significant global health concern, demanding timely detection and effective therapeutic interventions. Automated image classification via computational algorithms holds substantial promise in significantly improving the efficacy of clinical diagnoses. This study is committed to mitigating the challenge of diagnostic accuracy in the classification of multiclass skin lesions. This endeavor is inherently formidable owing to the resemblances among various lesions and the constraints associated with extracting precise global and local image features within diverse dimensional spaces using conventional convolutional neural network methodologies. Consequently, this study introduces the SkinSwinViT methodology for skin lesion classification, a pioneering model grounded in the Swin Transformer framework featuring a global attention mechanism. Leveraging the inherent cross-window attention mechanism within the Swin Transformer architecture, the model adeptly captures local features and interdependencies within skin lesion images while additionally incorporating a global self-attention mechanism to discern overarching features and contextual information effectively. The evaluation of the model’s performance involved the ISIC2018 challenge dataset. Furthermore, data augmentation techniques augmented training dataset size and enhanced model performance. Experimental results highlight the superiority of the SkinSwinViT method, achieving notable metrics of accuracy, recall, precision, specificity, and F1 score at 97.88%, 97.55%, 97.83%, 99.36%, and 97.79%, respectively.

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Section: Discussionmentioning

confidence: 93%

Section: Techniquementioning

confidence: 99%

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

Tang,

Su,

Chen

et al. 2024

Applied Sciences

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“…Authors in Reference 31, presented the deep learning and feature selection based technique for skin lesion classification and obtained the improved accuracy of 85.1. In Reference 32, authors obtained the accuracy of 90.2% for ISIC2018 dataset. The proposed framework obtained an improved accuracy of 95.7%.…”

Section: Resultsmentioning

confidence: 99%

SkinNet‐ENDO: Multiclass skin lesion recognition using deep neural network and Entropy‐Normal distribution optimization algorithm with ELM

Khan

Akram

Zhang

et al. 2023

Int J Imaging Syst Tech

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The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The manual screening of dermoscopic images is not an efficient procedure; therefore, researchers working in the domain of computer vision employed several algorithms to classify the skin lesion. The existing computerized methods have a few drawbacks, such as low accuracy and high computational time. Therefore, in this work, we proposed a novel deep learning and Entropy‐Normal Distribution Optimization Algorithm with extreme learning machine (NDOEM)‐based architecture for multiclass skin lesion classification. The proposed architecture consists of five fundamental steps. In the first step, two contrast enhancement techniques including hybridization of mathematical formulation and convolutional neural network are implemented prior to data augmentation. In the second step, two pre‐trained deep learning models, EfficientNetB0 and DarkNet19, are fine‐tuned and retrained through the transfer learning. In the third step, features are extracted from the fine‐tuned models and later the most discriminant features are selected based on novel Entropy‐NDOELM algorithm. The selected features are finally fused using a parallel correlation technique in the fourth step to generate the result feature vectors. Finally, the resultant features are again down‐sampled using the proposed algorithm and the resultant features are passed to the extreme learning machine (ELM) for the final classification. The simulations are conducted on three publicly available datasets as HAM10000, ISIC2018, and ISIC2019 to achieving an accuracy of 95.7%, 96.3%, and 94.8% respectively.

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“…Automatic diagnosis is split into three steps: skin lesion preprocessing and boundary estimation, feature extraction/selection and lesion classification based on these features [ 8 , 9 ]. Researchers have used various preprocessing techniques in the past decade to preprocess medical images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

et al. 2023

Self Cite

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The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results.

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BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification

Cited by 15 publications

References 55 publications

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities

SkinNet‐ENDO: Multiclass skin lesion recognition using deep neural network and Entropy‐Normal distribution optimization algorithm with ELM

An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

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