Can Deep Learning Revolutionize Skin Cancer Detection?

🔍 Key Finding This review analyzes deep learning techniques for skin cancer detection, finding convolutional neural networks (CNNs), particularly when combined with image segmentation and data augmentation, show superior performance compared to other machine learning algorithms in classifying skin lesions. However, challenges remain, including the need for larger, more diverse datasets representing various skin tones and lesion sizes, and more powerful hardware to facilitate efficient training.

🔬 Methodology Overview

• Design: Systematic Literature Review (SLR)
• Data Sources: IEEE, Scopus, Wiley, Google Scholar, Sprinkle, ACM, Science Direct
• Search Query: Keyword-based search using terms like “skin cancer,” “deep learning,” “machine learning,” “lesion detection,” etc. (See Table 3 for the search process)
• Selection Criteria: Publisher-based filtering (renowned publishers only), title-based filtering, abstract screening, and full-text review based on research questions and quality assessment criteria (See Table 6 and Section 3.5).
• Analysis Approach: Qualitative synthesis of findings from selected papers regarding deep learning techniques, datasets, challenges, and approaches for skin cancer detection.
• Scope: Deep learning and machine learning approaches for skin cancer detection and classification using medical images.
• Timeframe: Papers published between 2018-2022.

📊 Results

• Deep learning outperforms dermatologists in some studies: One study found a CNN outperformed 86.6% of dermatologists in melanoma diagnosis from dermoscopic images. Another showed a deep learning algorithm achieved 91.3% accuracy compared to dermatologists’ 74.4% sensitivity and 59% specificity in classifying skin lesions.
• Deep learning models achieve high accuracy in skin lesion classification: Studies reported accuracy rates ranging from 85.4% to 99% using various deep learning models (e.g., Transfer Learning, AlexNet, DCNN, FRCN) and datasets (e.g., PH2, ISBI). One study using a hybrid approach (AlexNet + VGG16) achieved 99% accuracy across multiple datasets.
• Segmentation improves deep learning performance: One study found deep learning classification was more accurate with segmented images compared to non-segmented images using the ISIC 2016 dataset.
• Data augmentation improves melanoma detection: One study using data augmentation and SqueezeNet achieved 92.18% accuracy in melanoma detection.
• Lesion size impacts diagnostic accuracy: Lesions smaller than 6mm pose a challenge for accurate melanoma identification, significantly reducing diagnostic sensitivity.
• Standard datasets lack diversity: Current datasets overrepresent light-skinned individuals, limiting the generalizability of trained models to diverse populations. More images of people of color are needed.
• Limited and imbalanced datasets are a challenge: Publicly available datasets are limited in size and often imbalanced, with fewer images of rarer skin cancer types, hindering model training and evaluation.

💡 Clinical Impact Deep learning algorithms, particularly CNNs, demonstrate comparable or superior accuracy to dermatologists in detecting skin cancer from dermoscopic images, potentially aiding in earlier diagnosis and improving patient outcomes. This technology could be integrated into clinical workflows to assist clinicians, particularly those with less experience, in identifying suspicious lesions and prioritizing biopsies.

🤔 Limitations

• Deep learning systems require extensive training, demanding substantial time and powerful hardware.
• Lesion size significantly impacts diagnostic accuracy, with small lesions (1-2mm) posing challenges.
• Standard datasets overrepresent light-skinned individuals, limiting accuracy for diverse populations.
• Small interclass variation between some skin lesions makes image analysis and classification difficult.
• Unbalanced datasets with varying image quantities per skin cancer type hinder reliable conclusions.
• Deep learning requires powerful hardware (GPUs) for image analysis, which may not be readily available.
• Standard datasets lack age-wise image distribution, potentially affecting accuracy for older individuals.

✨ What It Means For You This research suggests deep learning algorithms can improve skin cancer diagnostic accuracy, potentially exceeding the performance of some dermatologists, especially in classifying specific melanoma subtypes. This could lead to earlier and more accurate diagnoses, impacting treatment decisions and patient outcomes, though further research and validation are needed before widespread clinical implementation. Access to diverse and representative training datasets remains a key challenge for developing robust and equitable AI diagnostic tools.

Reference Mazhar T, Haq I, Ditta A, Mohsan SAH, Rehman F, Zafar I, Gansau JA, Goh LPW. The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer. Healthcare. 2023;11:415. https://doi.org/10.3390/healthcare11030415