Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.

Background: Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method: Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result: This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82-87%), the specificity was 86% (82-88%), and the C-index was 0.87 (0.84-0.90). Conclusion: The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.

Fractal characteristics of overburden fissures in shallow thick coal seam mining in loess gully areas.

The problems of water scarcity and ecological fragility are common in the loess gully area. To research the distribution and evolution of the overburden fissures and quantitatively analyze them have certain theoretical and engineering significance for realizing the evaluation of overburden damage degree and safe and green mining. This paper takes the 6102 working face of Chuancao Gedan Coal Mine as the engineering background. The development law and distribution characteristics of overburden fissures caused by the mining of shallow coal seams in the loess gully area were studied by the combination of physical similarity simulation, numerical similarity simulation and fractal theory. The results show that the fractal dimension change of the overburden fissures caused by the shallow mining of coal seam groups in the loess gully area can be divided into three stages during the mining process of the working face. Repeated mining causes the activation and development of overburden fissures, the fractal dimension increases significantly, and the regularity of changes weakens. The magnitude of the stress near the working face and the fluctuation times of the stress in the goaf have an influence on the change of the fractal dimension of the overburden fissures. According to the development angle and the fractal dimension of the overburden fissures, the overburden rock above the goaf is divided into the collapse fissure area, the compaction fissure area, and the vertical fissure area. Overburden fissures develop violently in the vertical fissure area, the overburden fissures in the compaction fissure area are mostly transverse fissures, and the overburden fissures in the caving fissure area are irregular.

Instability motion characteristics of overburden rock and the distribution pattern of fissures in shallow thick seam mining.

This paper analyzes the instability movement characteristics of overburden in shallow thick coal seam mining and its influence on the development and distribution of fault fractures. The similarity simulation experiment and theoretical analysis were combined based on the classification of the occurrence characteristics of the key bearing layer in the overburden rock of shallow thick seam mining. This study investigated the fracture characteristics and the instability motion mode of the key bearing layer in shallow thick seam mining and their effects on the distribution of fissures in the overburden rock. The results indicated that according to the horizon of the key bearing layer, the occurrence of overburden rock could be classified into 2 categories, i.e., the horizon of the key bearing layer within the caving zone and within the fissure zone. The horizon of the key bearing layer has a significant effect on the fracture characteristics and the instability motion mode of the key bearing layer. When the horizon of the key bearing layer is in the overburden caving zone, a "step rock beam" develops after fracture, and the instability motion mode is sliding instability. When the horizon of the key bearing layer is in the overburden fissure zone, a "masonry-like beam" develops after fracture, and the instability motion mode is rotary instability. The fracture instability of the key bearing layer could control the development and distribution of fissures in the overburden rock, and the whole favorable zone for the development of fissures extends along the advancing direction of the working face in a form of "diagonal stripes" with the instability motion of the key bearing layer.