AI Medical Compendium Journal:
Journal of imaging

Showing 1 to 10 of 11 articles

A Robust Machine Learning Model for Diabetic Retinopathy Classification.

Journal of imaging
Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classifi...

Harmonizing Image Forgery Detection & Localization: Fusion of Complementary Approaches.

Journal of imaging
Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and local...

High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K.

Journal of imaging
Vulnerable Road User (VRU) detection is a major application of object detection with the aim of helping reduce accidents in advanced driver-assistance systems and enabling the development of autonomous vehicles. Due to intrinsic complexity present in...

A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks.

Journal of imaging
Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed ...

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning.

Journal of imaging
An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that ma...

Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

Journal of imaging
In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely on the segmentation of independent PET slices. To exploit cross-slice information, typically overloo...

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review.

Journal of imaging
With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different ...

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images.

Journal of imaging
This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives informatio...

An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents.

Journal of imaging
In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decisi...

Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.

Journal of imaging
Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications-lesion detection and classification, thereby provid...