A new model for classification of medical CT images using CNN: a COVID-19 case study.

SARS-CoV-2 is the causative agent of COVID-19 and leaves characteristic impressions on chest Computed Tomography (CT) images in infected patients and this analysis is performed by radiologists through visual reading of lung images, and failures may occur. In this article, we propose a classification model, called Wavelet Convolutional Neural Network (WCNN) that aims to improve the differentiation of images of patients with COVID-19 from images of patients with other lung infections. The WCNN model was based on a Convolutional Neural Network (CNN) and wavelet transform.

Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma.

Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC.

Three-dimensional reconstruction and surface extraction of lower limbs as visualization methodologies of ecchymosis.

This paper presents a computational system for three-dimensional reconstruction and surface extraction of the human lower limb as a new methodology of visualizing images of multifaceted ecchymosis on the lower limbs. Through standardization of image acquisition by a mechanical system, an algorithm was developed for three-dimensional and surface reconstruction based on the extraction of depth from silhouettes. In order to validate this work, a three-dimensional model of the human lower limb was used inside a virtual environment.

Evaluation of the clinical efficacy of minimally invasive procedures for breast cancer screening at a teaching hospital.

Aims: To assess the clinical efficacy of diagnostic procedures for breast cancer at a teaching hospital using internal auditing tools and quality control measures.

Methods: A retrospective assessment of 500 patients who underwent core needle biopsy (wide-bore needle biopsy; WBN) of palpable or non-palpable breast nodes that were submitted for at least one cytological examination (fine needle aspiration (FNA) cytology and/or imprint of a WBN specimen). For statistical analysis the auditing tool and quality control proposed by the National Health Service breast screening programme was utilised.