Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach

Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma: Part II

Background: Melanoma, a highly aggressive form of skin cancer, necessitates early detection to significantly improve survival rates. Traditional diagnostic techniques, such as white-light imaging (WLI), are effective but often struggle to differentiate between melanoma subtypes in their early stages. Methods: The emergence of the Spectrum-Aided Vison Enhancer (SAVE) offers a promising alternative by utilizing specific wavelength bands to enhance visual contrast in melanoma lesions. This technique facilitates greater differentiation between malignant and benign tissues, particularly in challenging cases. In this study, the efficacy of the SAVE is evaluated in detecting melanoma subtypes including acral lentiginous melanoma (ALM), melanoma in situ (MIS), nodular melanoma (NM), and superficial spreading melanoma (SSM) compared to WLI. Results: The findings demonstrated that the SAVE consistently outperforms WLI across various key metrics, including precision, recall, F1-scorw, and mAP, making it a more reliable tool for early melanoma detection using the four different machine learning methods YOLOv10, Faster RCNN, Scaled YOLOv4, and YOLOv7. Conclusions: The ability of the SAVE to capture subtle spectral differences offers clinicians a new avenue for improving diagnostic accuracy and patient outcomes.

Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma: Part II

Background: Melanoma, a highly aggressive form of skin cancer, necessitates early detection to significantly improve survival rates. Traditional diagnostic techniques, such as white-light imaging (WLI), are effective but often struggle to differentiate between melanoma subtypes in their early stages. Methods: The emergence of the Spectrum-Aided Vison Enhancer (SAVE) offers a promising alternative by utilizing specific wavelength bands to enhance visual contrast in melanoma lesions. This technique facilitates greater differentiation between malignant and benign tissues, particularly in challenging cases. In this study, the efficacy of the SAVE is evaluated in detecting melanoma subtypes including acral lentiginous melanoma (ALM), melanoma in situ (MIS), nodular melanoma (NM), and superficial spreading melanoma (SSM) compared to WLI. Results: The findings demonstrated that the SAVE consistently outperforms WLI across various key metrics, including precision, recall, F1-scorw, and mAP, making it a more reliable tool for early melanoma detection using the four different machine learning methods YOLOv10, Faster RCNN, Scaled YOLOv4, and YOLOv7. Conclusions: The ability of the SAVE to capture subtle spectral differences offers clinicians a new avenue for improving diagnostic accuracy and patient outcomes.

Early cancer detection using deep learning and medical imaging: A survey

Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial for identifying various cancers, yet its manual interpretation by radiologists is often subjective, labour-intensive, and time-consuming. Consequently, there is a critical need for an automated decision-making process to enhance cancer detection and diagnosis. Previously, a lot of work was done on surveys of different cancer detection methods, and most of them were focused on specific cancers and limited techniques. This study presents a comprehensive survey of cancer detection methods. It entails a review of 99 research articles collected from the Web of Science, IEEE, and Scopus databases, published between 2020 and 2024. The scope of the study encompasses 12 types of cancer, including breast, cervical, ovarian, prostate, esophageal, liver, pancreatic, colon, lung, oral, brain, and skin cancers. This study discusses different cancer detection techniques, including medical imaging data, image preprocessing, segmentation, feature extraction, deep learning and transfer learning methods, and evaluation metrics. Eventually, we summarised the datasets and techniques with research challenges and limitations. Finally, we provide future directions for enhancing cancer detection techniques.

The causal relationships of granulocytes and melanoma skin cancer: A univariable and multivariable Mendelian randomization study

BACKGROUND

Increasing evidence has revealed that granulocyte has a critical role in tumorigenesis and progression. In this study, Mendelian randomization (MR) analysis was utilized for estimating the causal association between neutrophil percentage and melanoma skin cancer, eosinophil percentage and melanoma skin cancer, basophil percentage and melanoma skin cancer, respectively.

METHODS

The Genome-Wide Association Study (GWAS) ids for melanoma skin cancer, neutrophil percentage, eosinophil percentage and basophil percentage were derived from Integrative Epidemiology Unit (IEU) Open GWAS database. The univariable MR (UVMR) analysis was conducted to estimate the risk using MR-Egger, weighted median, inverse variance weighted (IVW). In addition, sensitivity analysis was conducted to assess the reliability of UVMR results. Finally, the multivariable MR (MVMR) analysis was performed to investigate causality between neutrophil percentage and eosinophil percentage in the presence of both and melanoma skin cancer.

RESULTS

The UVMR indicated that neutrophil percentage and eosinophil percentage were significantly and causally related to melanoma skin cancer, with neutrophil percentage [p = 0.025, odds ratio (OR) = 1.002] as a risk factor and eosinophil percentage (p = 7.04E-06, OR = 0.997) as a protective factor. Moreover, MVMR analysis indicated eosinophil percentage remained the protective factor (p = 0.003, OR = 0.998), while the causality of neutrophil percentage and melanoma skin cancer became insignificant (p > 0.05).

CONCLUSION

The causal relationships of neutrophil percentage and melanoma skin cancer, eosinophil percentage and melanoma skin cancer were shown by this study, which provided a reference for subsequent research and treatment related to melanoma skin cancer.