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Diagnosing skin cancer via C‐means segmentation with enhanced fuzzy optimization - Durgarao - 2021 - IET Image Processing - Wiley Online Library
Source: ietresearch.onlinelibrary.wiley.com
Article summary:
1. Skin cancer is a fast-growing disease with high mortality rates, and early diagnosis is crucial for survival.
2. Computer-assisted systems can provide reliable and faster results for early skin cancer diagnosis, and optimization algorithms like PSO and GA are becoming more common.
3. This paper proposes a novel skin cancer detection approach that involves segmentation, feature extraction, fuzzy classification, and an improved optimization algorithm called DOROA. The proposed approach shows promising results in detecting benign, malignant, or normal skin images.
Article analysis:
The article titled "Diagnosing skin cancer via C‐means segmentation with enhanced fuzzy optimization" discusses a novel approach to detecting skin cancer using segmentation, feature extraction, and fuzzy classification. The article highlights the increasing incidence of skin cancer and the need for early detection to improve survival rates.
The article provides a comprehensive review of related works in the field, including studies that have used neural networks and multi-frequency impedance spectra for skin cancer detection. However, the article does not provide a critical analysis of these studies or discuss their limitations. This lack of critical analysis may suggest bias towards the proposed approach.
The proposed approach involves using FCM for segmentation and hybrid LVP and LBP for feature extraction. The classification process is done using a fuzzy classifier that takes the mined LVP+LBP features as input. The article claims that this approach improves performance compared to traditional methods but does not provide sufficient evidence to support this claim.
One potential bias in the article is its focus on the proposed approach without adequately considering other approaches or potential counterarguments. Additionally, there is no discussion of possible risks associated with computer-assisted diagnosis or potential limitations of the proposed algorithm.
Overall, while the article provides an interesting perspective on skin cancer detection using fuzzy optimization, it lacks critical analysis and evidence to support its claims fully. Further research is needed to validate the effectiveness of this approach and compare it with other existing methods.
Topics for further research:
Limitations of neural networks for skin cancer detection
Multi-frequency impedance spectra for skin cancer diagnosis: pros and cons
Risks associated with computer-assisted diagnosis of skin cancer
Comparison of fuzzy classification with other classification methods for skin cancer detection
Potential drawbacks of using FCM for skin cancer segmentation
Validation studies of skin cancer detection algorithms using real-world data