Background Mammography has established itself as the most efficient technique for the
identification of the pathological breast lesions. Among the various types of lesions …

In the last decade, the analysis of the medical images has evolved significantly, applications
and tools capable to extract quantitative characteristics of the images beyond the …

This study addresses the classification problem of the HEp-2 cells using indirect
immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune …

Introduction Breast cancer is globally a major threat for women's health. Screening and
adequate follow-up can significantly reduce the mortality from breast cancer. Human second …

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune
diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and …

Neurodegenerative diseases are frequently associated with structural changes in the brain.
Magnetic resonance imaging (MRI) scans can show these variations and therefore can be …

Objectives This study aimed to develop a deep learning radiomic model using multimodal
imaging to differentiate benign and malignant breast tumours. Methods Multimodality …

We present a scheme for the feature extraction and classification of the fluorescence
staining patterns of HEp-2 cells in IIF images. We propose a set of complementary …

Background This work investigates the applicability of a novel clustering approach to the
segmentation of mammographic digital images. The chaotic map clustering algorithm is …

In previous chapters, we discussed the usage of AI in healthcare, medical imaging, and
detection of breast cancer with various points. In this chapter, various image datasets for the …