Detecting skin lesions fusing handcrafted features in image network ensembles

Sharafudeen, Misaj; S, Vinod Chandra S

doi:10.1007/s11042-022-13046-0

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…Relative to alternative models for skin lesion classification, our proposed SkinSwinViT model exhibits superior advancement. 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.…”

Section: Discussionmentioning

confidence: 99%

“…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: 99%

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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“…A mix of images, hand-extracted features, and metadata is used in [131] to perform a multiclass classification based on ensemble networks. Multiple multi-input single-output (MISO) models, obtained by replacing the backbones with EfficientNet networks B4 to B7, are trained with the images to extract features, whereas the hand-extracted features and metadata are used for training an MPL with two dense layers.…”

Section: Ml/dl Hybrid Techniquesmentioning

confidence: 99%

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

et al. 2022

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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.

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“…CNN uses its deep layers as feature extractors [25]. CNN features are trained using training data, whereas experts craft HC features to determine a particular set of attributes [26]. The need to compute resources and the accessibility of huge training sets are the primary barriers to using a CNN as an effective feature extractor [27].…”

Section: Introductionmentioning

confidence: 99%

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Naeem,

Anees,

Khalil

et al. 2024

Mathematics

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The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

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Detecting skin lesions fusing handcrafted features in image network ensembles

Cited by 16 publications

References 17 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

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

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

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