2021
A Dynamic Quantum Clustering Approach to Brain Tumor Segmentation
Jacksson Sánchez, Miguel Martin Landrove
arXiv:2107.07698v2 [physics.med-ph], 25 Jul 2021
Data clustering has been widely used in data analysis and classification. In the present work, a method based on dynamic quantum clustering is proposed for the segmentation and analysis of brain tumor MRI. The results open the possibility of applications to multi modality medical imaging.
Use of the quantum cluster algorithm and scaling dynamics in magnetic resonance imaging for prostate cancer staging
J Reyes Bruno, F Torres-Hoyos, R Baena-Navarro
2021 J. Phys.: Conf. Ser. 2046 012007
Malignant tumors in the prostate generally have amorphous geometries and roughness is evident on their surface. These characteristics, together with the escalation dynamics of this type of lesion, provide information on their degree of malignancy or stage, therefore, their understanding leads to establishing an adequate diagnosis and a good radiotherapeutic implementation. The geometry present in prostate cancers leads to an analysis based on scales and a fractal study. In the present work, an in vivo diagnosis of prostate cancer will be made with magnetic resonance images, three-dimensional, using the cluster quantum algorithm and the calculation of the critical exponents of local roughness and the fractal dimension, which will allow staging said lesions. and whose results are consistent with those reported in the literature.
Support Vector Machine Technique as Classifier of Impaired Body Fat Percentage
Alexandra La Cruz, Erika Severeyn, Mónica Huerta, Sara Wong
Fuzzy Systems and Data Mining VII, 2021
Excess weight and obesity are indicators of an unhealthy or harmful accumulation of fat that can be dangerous to health. Body mass index (BMI) refers to height-to-weight radio and is often used to identify overweight and obesity in adults. Although BMI is commonly used to diagnose obesity and overweight, it is ineffective in differentiating between high muscle mass and elevated body fat mass. Body fat percentage (BF%) is one of the best predictors of obesity because it quantifies adipose tissue. The Deurenberg equation is among the indirect methods to measure BF%; it uses BMI, age, and sex as parameters to calculate the BF%. Machine learning techniques demonstrated to be a good classifier of overweight, obesity, and diseases related to insulin resistance and metabolic syndrome. This study intends to evaluate anthropometric parameters as classifiers of BF% alteration using support vector machines and the Deurenberg equation for BF% estimation. The database used consisted of 1978 individuals with 24 different anthropometric measurements. The results suggest the SVM as a suitable technique for classifying individuals with normal and abnormal BF% values. Accuracy, F1 score, PPV, NPV, and sensitivity were above 0.8. Besides, the specificity value is below 0.7, which indicates that false positives may occur. As future work, this research intends to apply neural networks as a classification technique.
