For the past two decades, most of the people from develo** countries are suffering from
heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of …

Women who have recovered from breast cancer (BC) always fear its recurrence. The fact
that they have endured the painstaking treatment makes recurrence their greatest fear …

An experimental comparison of a large number of different image descriptors for content-
based image retrieval is presented. Many of the papers describing new techniques and …

Automatic analysis of histopathological images has been widely utilized leveraging
computational image-processing methods and modern machine learning techniques. Both …

Content-based medical image retrieval (CBMIR) is one of the most challenging and
ambiguous tasks used to minimize the semantic gap between images and human queries in …

This paper introduces a three-step framework for classifying multiclass radiography images.
The first step utilizes a de-noising technique based on wavelet transform (WT) and the …

A content-based image retrieval (CBIR) framework for diverse collection of medical images
of different imaging modalities, anatomic regions with different orientations and biological …

This paper presents a classification-driven biomedical image retrieval framework based on
image filtering and similarity fusion by employing supervised learning techniques. In this …

Closing the semantic gap in medical image analysis is critical. Access to large-scale
datasets might help to narrow the gap. However, large and balanced datasets may not …

As the network and development of multimedia technologies are becoming more popular,
users are not satisfied with the traditional information retrieval techniques. so nowadays the …