Benign skin lesions classification
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Deep Learning and CNNs for Benign Skin Lesion Classification
Recent research shows that deep learning, especially convolutional neural networks (CNNs), is highly effective for classifying benign skin lesions. Multiple studies have used pre-trained models like ResNet50, VGG-16, AlexNet, and GoogleNet, often combined with transfer learning and data augmentation, to improve accuracy in distinguishing benign from malignant lesions and among various benign types Salamaa2021Salma2022Albahar2019+5 MORE. These models extract important features such as texture, shape, and color patterns from dermoscopic images, which are crucial for accurate classification Dhanalakshmi2025Kassem2020Hosny2020.
Hybrid and Ensemble Approaches in Skin Lesion Classification
Several studies have improved classification performance by combining CNNs with other machine learning techniques. For example, replacing the fully connected layer of CNNs with a Support Vector Machine (SVM) classifier has led to higher accuracy and better generalization Salamaa2021Salma2022Mahbod2017. Ensemble methods, such as stacking multiple classifiers or fusing outputs from different CNN architectures, have also shown to boost performance, especially in distinguishing between benign, dysplastic, and malignant lesions Ghalejoogh2020Mahbod2017.
Preprocessing and Data Augmentation for Improved Accuracy
Preprocessing steps like noise removal, contrast enhancement, and artifact (e.g., hair) removal are commonly used to improve image quality before classification Salamaa2021Salma2022. Data augmentation techniques, such as image rotation and flipping, help address the challenge of limited labeled data and further enhance model robustness and accuracy Salamaa2021Salma2022Sun2021.
Use of Patient Metadata and Multiclass Classification
In addition to image data, incorporating patient metadata (such as age and sex) into the classification model has been shown to improve accuracy, especially in multiclass settings where the goal is to distinguish between several types of benign and malignant lesions . Models have successfully classified skin lesions into multiple classes, including benign keratosis, melanocytic nevus, dermatofibroma, and vascular lesions, with high accuracy and balanced performance Sun2021Kassem2020Hosny2020.
Alternative Machine Learning Methods
While deep learning dominates recent research, traditional machine learning methods like K-nearest neighbor (KNN) have also been used for benign skin lesion classification, achieving high accuracy and fast computation, making them suitable for quick screening applications .
Clinical Relevance and Practical Application
Automated classification systems using these advanced techniques can assist dermatologists by providing fast, accurate, and scalable screening tools. This can lead to earlier detection, reduced human error, and better patient outcomes, especially in distinguishing benign from malignant lesions and among different benign types Salma2022Albahar2019Dhanalakshmi2025+2 MORE.
Conclusion
In summary, the classification of benign skin lesions has greatly benefited from deep learning, hybrid models, and data augmentation. Combining CNNs with SVMs or ensemble methods, using preprocessing, and integrating patient metadata all contribute to high accuracy and practical utility in clinical settings. These advancements support more reliable and efficient diagnosis of benign skin lesions, aiding both clinicians and patients.
Sources and full results
Most relevant research papers on this topic
Automated deep learning approach for classification of malignant melanoma and benign skin lesions
The proposed automated skin lesion classification system using ResNet50 architecture combined with SVM achieves high accuracy and low computational complexity, aiding medical practitioners in identifying malignant and benign skin lesions.
Skin Lesion Classification Using Additional Patient Information
Our algorithm for skin lesion classification using additional patient information achieves better accuracy than previous methods, making it the first ranked algorithm on the live leaderboard.
Classification of Benign and Malignant Skin Lesions using Convolutional Neural Networks (CNN)
Deep learning can accurately classify skin lesions into malignant (melanoma, basal cell carcinoma, squamous cell carcinoma) and benign (seborrheic keratosis) categories, improving early detection and patient outcomes.
Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet
The proposed method accurately classifies skin lesions into seven classes using transfer learning with pre-trained AlexNet, with 98.70% accuracy, sensitivity, specificity, and precision.
A hierarchical structure based on Stacking approach for skin lesion classification
The HSBS method outperforms the SBS approach in classifying skin lesions as benign, dysplastic, and melanoma, improving the performance of automated skin cancer diagnostic systems.
Skin Lesion Classification Using Hybrid Deep Neural Networks
The proposed method using hybrid deep neural networks achieves very good skin lesion classification performance, with 83.83% accuracy for melanoma and 97.55% for seborrheic keratosis.
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