A survey of machine learning-based methods for COVID-19 medical image analysis

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.

Offline and Online Interactive Frameworks for MRI and CT Image Analysis in the Healthcare Domain : The Case of COVID-19, Brain Tumors and Pancreatic Tumors

Medical imaging represents the organs, tissues and structures underneath the outer layers of skin and bones etc. and stores information on normal anatomical structures for abnormality detection and diagnosis. In this thesis, tools and techniques are used to automate the analysis of medical images, emphasizing the detection of brain tumor anomalies from brain MRIs, Covid infections from lung CT images and pancreatic tumor from pancreatic CT images. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning models, and recent deep learning models are used. The following problems for medical images : abnormality detection, abnormal region segmentation, interactive user interface to represent the results of detection and segmentation while receiving feedbacks from healthcare professionals to improve the analysis procedure, and finally report generation, are considered in this research. Complete interactive systems containing conventional models, machine learning, and deep learning methods for different types of medical abnormalities have been proposed and developed in this thesis. The experimental results show promising outcomes that has led to the incorporation of the methods for the proposed solutions based on the observations of the performance metrics and their comparisons. Although currently separate systems have been developed for brain tumor, Covid and pancreatic cancer, the success of the developed systems show a promising potential to combine them to form a generalized system for analyzing medical imaging of different types collected from any organs to detect any type of abnormalities

The use of a figure-of-merit (FOM) for optimization in digital mammography: an exploratory study in Malta

This PhD thesis comprises an exploratory study in digital mammography physics that portrays two essential components. The first component (1) presents the first national survey of the technical performance of mammography equipment in Malta using the European Protocol [1-3]. This demonstrated considerable differences in the technical performance of the mammography units across the country with a wide range in performance, patient dose and image quality. A common problem was that many clinics had implemented computed radiography (CR) systems to replace existing film-screen (FS) systems without due consideration to optimization. All direct digital (DR) mammography units met current international technical performance standards and the effectiveness of DR mammography in reducing patient dose and maintaining high image quality compared to CR has been confirmed. The second component (2) was to explore the use of a figure-of-merit (FOM) for optimization and characterisation in digital mammography. The use of image quality parameters in digital mammography such as contrast-to-noise ratio (CNR) or signal-difference-to-noise ratio (SDNR), signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) have been traditionally used for the quantitative evaluation of the system performance against international standards or guidelines. The use of FOMs is relatively new and may be considered as a new quality assurance tool in digital mammography permitting the quantitative and simultaneous assessment of image quality and patient dose. The main objective in having a FOM is to have a numerical expression representing the efficiency and efficacy of a given system gauging how good or poor a system is performing. This may be useful in optimization and in predicting a predetermined or expected image quality with a given amount of radiation dose for a given system. The most interesting aspect of the FOMs in this work will be to investigate and explore the possibility for inter-system comparison

Diferencias biológicas y de supervivencia según el método de detección en pacientes con cáncer de mama

Existe consenso general en que los programas de “screening” o cribado con mamografía puede reducir la mortalidad por cáncer de mama un 20%; sin embargo, dichos programas están sometidos a varios sesgos como el de adelanto diagnóstico (“lead bias”) que se relaciona sobre todo con el tamaño tumoral, y el de duración de la enfermedad (“length bias”), relacionado con características biológicas y de proliferación de los tumores. Numerosos artículos han mostrado una relación entre tumores diagnosticados por cribado y menor tamaño, condiciones biológicas más favorables y mayor supervivencia, a pesar de lo cual el método de detección de la enfermedad parece ser un factor pronóstico independiente de dichas variables. El objetivo de este estudio fue medir las características biológicas implicadas en la génesis tumoral y la progresión del cáncer de mama en los carcinomas sintomáticos y los de cribado para identificar posibles diferencias, distinguiendo además entre tumores diagnosticados en la primera (prevalencia) y sucesivas rondas de screening (incidentes). Para este fin, se evaluaron parámetros clínico-patológicos y actividades de proliferación y apoptosis en una serie de 130 casos sintomáticos y 161 tumores detectados mediante cribado, de los que 76 fueron prevalentes, 65 incidentes y se incluyeron 20 casos de falso negativo. Tras ajustar por el tamaño pequeño de los carcinomas detectados mediante cribado en comparación con los cánceres sintomáticos, los detectados en el programa de cribado presentaron mayor supervivencia libre de enfermedad (RR = 0,43 IC = 0,19 - 0,96) y tenían concentraciones de receptores de estrógeno y progesterona más a menudo que los cánceres sintomáticos (OR = 3,38 IC = 1,72-6,63 y OR = 3,44 IC = 1,94-6,10, respectivamente). Además, la expresión de bcl-2, un marcador de buen pronóstico en el cáncer de mama, fue mayor y la expresión de HER2 / neu fue menor en los cánceres detectados mediante cribado que en los cánceres sintomáticos (OR = 1,77 IC = 1.1 - 3.23 y OR = 0,64 IC = 0,40-0,98, respectivamente). Sin embargo, al comparar carcinomas detectados mediante cribado prevalentes vs incidentes, los tumores prevalentes fueron de mayor tamaño (OR = 2,84, IC = 1.05- 7.69), tenían menos probabilidades de ser HER2 / neu positivo (OR = 0,22, IC = 0,08-0,61) y presentaron menor expresión Ki67 (OR = 0,36 IC = 0,17 - 0,77). Además, los tumores incidentes presentaron un tiempo de supervivencia más corta que los prevalentes (RR = 4,88 IC = 1,12 - 21,19). Conclusiones: Los carcinomas de incidencia incluyen una variedad de carcinomas detectados por screening que presentan diferencias en la biología y el pronóstico en relación con carcinomas prevalentes. El método de detección es importante y debe tenerse en cuenta cuando se toman decisiones de terapia adyuvante