Recently medical image classification plays a vital role in retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior previous approaches that depend on handcrafted features; it remains difficult implement because of the high intra-class variance inter-class similarity generated by wide range imaging modalities clinical diseases. The Internet Things (IoT) he...

Medical image classification has played an important role in the medical field, and related method based on deep learning become powerful technique classification. In this article, we propose a simplified inception module Hadamard attention (SI + HA) mechanism for Specifically, new mechanism: mechanism. It improves accuracy of without greatly increasing complexity model. Meanwhile, adopt to imp...

AbstractMedical image classification has been widely adopted in medical analysis. However, due to the difficulty of collecting and labeling data area, datasets are usually highly-imbalanced. To address this problem, previous works utilized class samples as prior for re-weighting or re-sampling but feature representation is still not discriminative enough. In paper, we adopt contrastive learning...

This paper proposes a new approach to the processing and analysis of medical images. We introduce the term and methodology of medical data understanding, as a new step in the way of starting from image processing, and followed by analysis and classification (recognition). The general view of the situation of the new technology of machine perception and image understanding in the context of the ...

classification of vegetation according to their species composition is one of the most important tasks in the application of remote sensing in precision agriculture. to prepare an algorithm for such a mandate, there is a need for ground truth. field operation is very costly and time consuming. therefore, some other method must be developed, such as extracting information from the satellite imag...

This article presents the participation of the MIILab (Medical Image Information Laboratory) group in ImageCLEFmed2013. There are three types of tasks for ImageCLEFmed2013: modality classification, image retrieval and compound–image separation. Image modality classification and medical image retrieval are targeted according to MIILab’s research interest. The main goal is to perform a feasibilit...

Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports. ...

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...

Automatic medical image classification is a technique for assigning a medical image to a class among a number of image categories. Due to computational complexity, it is an important task in the content-based image retrieval (CBIR). In this paper, we propose a hierarchical medical image classification method including two levels using a perfect set of various shape and texture features. Further...

the main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. the major objective of this paper is to investigate the potential of a neuro-fuz...