Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Furriel, Brunna C. R. S.; Oliveira, Bruno D.; Prôa, Renata; Paiva, Joselisa Q.; Loureiro, Rafael M.; Calixto, Wesley P.; Reis, Márcio R. C.; Giavina-Bianchi, Mara

doi:10.3389/fmed.2023.1305954

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…Conversely, manual segmentation, while precise, is time-consuming and subject to interobserver and intraobserver variations. The combination of automated segmentation and human review emerges as a balanced solution, allowing validation and adjustments by experts, ensuring precise and reliable clinical decisions [ 114 – 116 ].…”

Section: Resultsmentioning

confidence: 99%

Unsupervised model for structure segmentation applied to brain computed tomography

dos Santos,

Scoczynski Ribeiro Martins,

Amorim Nogueira

et al. 2024

PLoS ONE

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This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.

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Section: Resultsmentioning

confidence: 99%

Unsupervised model for structure segmentation applied to brain computed tomography

dos Santos,

Scoczynski Ribeiro Martins,

Amorim Nogueira

et al. 2024

PLoS ONE

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“…FixCaps enhanced dermoscopy image categorization in [ 56 ]. FixCaps uses a huge in-height presentation kernel, 31 × 31, at the lowest convolution layer instead of the more common 9 × 9.…”

Section: Related Workmentioning

confidence: 99%

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Behara,

Bhero,

Agee

2024

Diagnostics

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Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diagnosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obstacles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate classification. As such, skilled clinicians often struggle to differentiate benign moles from early malignant tumors in skin images. Although deep learning-based approaches such as convolution neural networks have made significant improvements, their stability and generalization continue to experience difficulties, and their performance in accurately delineating lesion borders, capturing refined spatial connections among features, and using contextual information for classification is suboptimal. To address these limitations, we propose a novel approach for skin lesion classification that combines snake models of active contour (AC) segmentation, ResNet50 for feature extraction, and a capsule network with a fusion of lightweight attention mechanisms to attain the different feature channels and spatial regions within feature maps, enhance the feature discrimination, and improve accuracy. We employed the stochastic gradient descent (SGD) optimization algorithm to optimize the model’s parameters. The proposed model is implemented on publicly available datasets, namely, HAM10000 and ISIC 2020. The experimental results showed that the proposed model achieved an accuracy of 98% and AUC-ROC of 97.3%, showcasing substantial potential in terms of effective model generalization compared to existing state-of-the-art (SOTA) approaches. These results highlight the potential for our approach to reshape automated dermatological diagnosis and provide a helpful tool for medical practitioners.

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“…The landscape of clinical imaging is currently undergoing a revolutionary metamorphosis (contribution 1), with every sector impacted by the integration of artificial intelligence (AI). This paradigm shift is not confined to one specific domain but spans the diverse realms of medical imaging, encompassing imaging diagnostics for organs and functionality [ 1 , 2 ], the dynamic field of digital pathology (encompassing both cytology and digital histology) [ 3 , 4 ], the intricacies of digital dermatology [ 5 , 6 ], and various other niches within the expansive field of clinical imaging.…”

Section: The Joint Expedition Exploring Clinical Medical Imaging and ...mentioning

confidence: 99%

Joint Expedition: Exploring Clinical Medical Imaging and Artificial Intelligence as a Team Integration

Giansanti

2024

Diagnostics

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Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Cited by 13 publications

References 37 publications

Unsupervised model for structure segmentation applied to brain computed tomography

Unsupervised model for structure segmentation applied to brain computed tomography

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Joint Expedition: Exploring Clinical Medical Imaging and Artificial Intelligence as a Team Integration

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