Abstract
Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.
Highlights
The number of successful applications of machine learning algorithms is constantly growing
They successfully cope with inaccurate or noisy data, different sizes and orientations of objects, as well as, varying lighting conditions. If these algorithms are properly selected and trained, they have a high capacity to generalise the acquired knowledge. The latter is extremely important in practical applications where we have to struggle with a variety of cases, small, yet significant differences between classes, and a large diversity of objects within a class or an insufficient number of appropriately labelled unbalanced data
The paper is organised as follows: further, we present the medical aspect of cerebral microbleeds as well as related works regarding CMB detection and challenges in this field; in Section 2, we introduce the reader to the case study, our approach including algorithms and data handling, while in Section 3, we describe conducted experiments and deliver the results
Summary
The number of successful applications of machine learning algorithms is constantly growing. Deep neural networks (DNNs) are naturally predisposed to efficiently handle vast amounts of data They successfully cope with inaccurate or noisy data, different sizes and orientations of objects, as well as, varying lighting conditions. If these algorithms are properly selected and trained, they have a high capacity to generalise the acquired knowledge. The latter is extremely important in practical applications where we have to struggle with a variety of cases, small, yet significant differences between classes, and a large diversity of objects within a class or an insufficient number of appropriately labelled unbalanced data.
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