This paper proposes transfer learning and fine-tuning techniques for a deep learning model
to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this …

In the evolving field of Content-Based Image Retrieval (CBIR), we introduce a novel
approach that integrates deep learning models—NASNetMobile, DenseNet121, and VGG16 …

Content-based image retrieval (CBIR) systems are designed to retrieve images that are
relevant, based on detailed analysis of latent image characteristics, thus eliminating the …

Abstract Systems for retrieving and managing content-based medical images are becoming
more important, especially as medical imaging technology advances and the medical image …

Object detection is a fundamental but very important task in computer vision. Most current
algorithms require high computing resources, which hinders their deployment on embedded …

Radiologists or other clinical professionals must spend a lot of time and effort segmenting,
detecting, and extracting the infected tumor area from magnetic resonance (MR) images …

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …

Abstract Medical Image Analysis (MIA) is one of the active research areas in computer
vision, where brain tumor detection is the most investigated domain among researchers due …

In object detection with deep neural networks, the box-wise objectness score tends to be
overconfident, sometimes even indicating high confidence in presence of incorrect …

Object detection is an important basis for understanding the high-level semantic information
of images. To address the problems of small object accuracy and inaccurate bounding box …