Skin cancer diagnosis methods

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Overview of Skin Cancer Diagnosis Methods

Skin cancer diagnosis has evolved significantly, with a strong focus on early detection to improve patient outcomes. Modern approaches combine traditional clinical evaluation with advanced non-invasive imaging, artificial intelligence (AI), and machine learning (ML) techniques to enhance accuracy and reduce unnecessary procedures Narayanamurthy2018Myślicka2024.

Non-Invasive Diagnostic Techniques for Skin Cancer

Non-invasive methods are increasingly favored for their ability to detect skin cancer without the need for surgical biopsies. These include:

Photography and Dermoscopy: High-resolution images and dermoscopic analysis help dermatologists visually assess lesions .
Dynamic Thermal Imaging (DTI): This technique cools the lesion and records its thermal recovery using infrared imaging. DTI has demonstrated very high sensitivity and specificity (over 99%) in distinguishing cancerous from non-cancerous lesions, making it one of the most accurate non-invasive methods available .
Other Imaging Modalities: Sonography, confocal microscopy, Raman and fluorescence spectroscopy, terahertz spectroscopy, optical coherence tomography, multispectral imaging, thermography, and electrical bio-impedance are also used to provide detailed information about skin lesions .

Computer-Aided and AI-Based Skin Cancer Diagnosis

Image Processing and Feature Extraction

Automated systems use image processing to enhance and segment skin lesion images. Feature extraction techniques, such as bag-of-features and speeded up robust features, are crucial for distinguishing between cancerous and non-cancerous lesions. These features are then used by classifiers like support vector machines (SVM) to make diagnostic decisions Xu2020Arora2020.

Deep Learning and Convolutional Neural Networks (CNNs)

Deep learning, particularly CNNs, has become a cornerstone in skin cancer diagnosis. Optimized CNNs, sometimes enhanced with algorithms like whale optimization or sand cat swarm optimization, can automatically learn complex patterns from images, leading to improved diagnostic accuracy compared to traditional methods Akilandasowmya2023Zhang2020Xu2020.

Ensemble and Hybrid Approaches

Combining multiple machine learning and deep learning models—known as ensemble methods—further boosts diagnostic performance. Techniques like Max Voting aggregate predictions from models such as random forests, SVMs, and neural networks, achieving high accuracy, precision, and recall. These ensemble approaches have outperformed individual models and even expert dermatologists in some studies Akilandasowmya2023Natha2025Tembhurne2023.

Performance and Clinical Impact

Recent studies report that advanced AI-powered and ensemble methods can achieve diagnostic accuracies exceeding 90%, with some models reaching up to 94.7% accuracy and near-perfect sensitivity and specificity Akilandasowmya2023Godoy2017Natha2025+1 MORE. These systems are particularly valuable in settings with limited access to dermatology specialists and can help reduce unnecessary biopsies by improving specificity Godoy2017Melarkode2023.

Challenges and Future Directions

Despite significant progress, challenges remain, including the need for large, diverse datasets, standardization of evaluation metrics, and integration into clinical workflows. Ongoing research aims to address these issues and further refine AI-based diagnostic tools for broader clinical adoption Melarkode2023Myślicka2024.

Conclusion

Skin cancer diagnosis methods have rapidly advanced, with non-invasive imaging and AI-driven analysis now playing central roles. These technologies offer high accuracy, reduce the need for invasive procedures, and support early detection, ultimately improving patient care and outcomes Akilandasowmya2023Godoy2017Zhang2020+7 MORE.

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Most relevant research papers on this topic

Skin cancer diagnosis: Leveraging deep hidden features and ensemble classifiers for early detection and classification

The proposed deep hidden feature and ensemble classifier skin cancer detection and classification technique improves prediction accuracy compared to state-of-the-art classifiers, enhancing early skin cancer diagnosis.

Highly Cited

2023·

83citations

·G. Akilandasowmya et al.

Biomed. Signal Process. Control.·

DOI

Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging.

Dynamic thermal imaging (DTI) provides a rapid, accurate, and non-invasive method for detecting skin cancer, with a sensitivity in excess of 99% and prescribed specificity in excess of 99%.

2017·

52citations

·S. E. Godoy et al.

Biomedical optics express·

DOI

Skin cancer diagnosis based on optimized convolutional neural network

The optimized convolutional neural network (CNN) method using improved whale optimization algorithm outperforms other methods in early skin cancer detection.

Highly Cited

2020·

268citations

·Ning Zhang et al.

Artificial intelligence in medicine·

DOI

Boosting skin cancer diagnosis accuracy with ensemble approach

The Max Voting method, combining various machine learning models, significantly improves skin cancer diagnosis accuracy, reaching 94.70% and providing the most reliable and robust method for classification.

2025·

26citations

·Priya Natha et al.

Scientific Reports·

DOI

Computer-aided diagnosis of skin cancer based on soft computing techniques

The computer-aided method developed in this study effectively detects skin cancer early, with high accuracy, sensitivity, and positive predictive value compared to other methods.

Highly Cited

2020·

251citations

·Zhiying Xu et al.

Open Medicine·

DOI

Bag of feature and support vector machine based early diagnosis of skin cancer

The proposed method fusion of bag-of-features with speeded up robust features and quadratic support vector machine improves early skin cancer diagnosis accuracy by 3% compared to state-of-the-art methods.

2020·

55citations

·Ginni Arora et al.

Neural Computing and Applications·

DOI

Skin cancer detection using non-invasive techniques

Non-invasive skin cancer diagnostic methods, such as photography, dermoscopy, sonography, and confocal microscopy, offer promising alternatives to traditional invasive techniques, offering potential for improved detection and diagnosis.

Literature ReviewHighly Cited

2018·

119citations

·Vigneswaran Narayanamurthy et al.

RSC Advances·

DOI

Skin cancer detection using ensemble of machine learning and deep learning techniques

Our novel approach combining machine learning and deep learning techniques achieves a higher accuracy of 93% for skin cancer detection, outperforming expert dermatologists and other state-of-the-art methods.

Highly Cited

2023·

152citations

·Jitendra V. Tembhurne et al.

Multimedia Tools and Applications·

DOI

AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

AI-based methods can aid in early skin cancer detection, reducing morbidity and mortality rates.

Literature ReviewHighly Cited

2023·

121citations

·N. Melarkode et al.

Cancers·

DOI

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

Modern digital image processing methods, including artificial intelligence, can significantly improve skin cancer diagnostics and early detection, reducing deaths due to the disease.

Literature Review

2024·

16citations

·Maria Myślicka et al.

Archives of Dermatological Research·

DOI

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