DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem, Ahmad; Anees, Tayyaba

doi:10.1371/journal.pone.0297667

Cited by 4 publications

(1 citation statement)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Skin cancer is the most prevalent form of cancer. Clinical screening initiates the diagnostic procedure that is followed by a histological examination such as dermoscopy and biopsy [1]. Skin cancer is ultimately caused by DNA mutations that disrupt the normal growth of skin cells, resulting in cancer.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

Pacal,

Alaftekin,

Zengul

2024

J Digit Imaging. Inform. med.

View full text Add to dashboard Cite

Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning–based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature.

show abstract

A Review on Skin Melanoma Classification using different ML and DL Models

Viranchkumar Mayurbhai Kadia,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

View full text Add to dashboard Cite

This review explores the classification of skin melanoma utilizing various machine learning (ML) and deep learning (DL) models, highlighting the advancements and comparative performance of these methodologies. Skin melanoma, a serious type of skin cancer, demands early and accurate diagnosis for effective treatment. The review covers a range of ML techniques such as support vector machines, decision trees, and ensemble methods, alongside \DL approaches including convolutional neural networks and recurrent neural networks. Emphasis is placed on the models' accuracy, computational efficiency, and the datasets used for training and validation. The review underscores the potential of DL models to outperform traditional ML methods due to their ability to automatically extract and learn intricate features from large datasets, thus offering promising prospects for enhanced diagnostic precision in melanoma classification.

show abstract

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Cited by 4 publications

References 75 publications

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

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

Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

A Review on Skin Melanoma Classification using different ML and DL Models

Contact Info

Product

Resources

About