A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment.

Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning.

Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss.

Machine Learning for Early Parkinson's Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features.

Early Parkinson's Disease (PD) diagnosis is a critical challenge in the treatment process. Meeting this challenge allows appropriate planning for patients. However, Scan Without Evidence of Dopaminergic Deficit (SWEDD) is a heterogeneous group of PD patients and Healthy Controls (HC) in clinical and imaging features.

Anatomical prognosis after idiopathic macular hole surgery: machine learning based-predection.

To develop a machine learning (ML) model for the prediction of the idiopathic macular hole (MH) status at 9 months after vitrectomy and inverted flap internal limiting membrane (ILM) peeling surgery. This single center was conducted at Department A, Institute Hedi Raies of Ophthalmology, Tunis, Tunisia. The study included 114 patients.

A bilinear convolutional neural network for lung nodules classification on CT images.

Purpose: Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images.

Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey.

Background: Lung cancer is the most common cancer in the world. Computed tomography (CT) is the standard medical imaging modality for early lung nodule detection and diagnosis that improves patient's survival rate. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have become a preferred methodology for developing computer-aided detection and diagnosis (CAD) schemes of lung CT images.

Automatic detection of intracranial aneurysm using LBP and Fourier descriptor in angiographic images.

Purpose: Intracranial aneurysms (IA) are abnormal dilatation of the arteries at the circle of Willis whose rupture can lead to catastrophic complications such as hemorrhagic stroke. The purpose of this work is to detect IA in 2D-DSA images. The proposed detection framework uses local binary patterns for the determination of initial aneurysm candidates and generic Fourier descriptor (GFD) for false positive removal.

A priori knowledge integration for the detection of cerebral aneurysm.

The detection of intracranial aneurysms is of a paramount effect in the prevention of cerebral subarachnoid hemorrhage. We propose in this paper, a new approach to detect cerebral aneurysm in digital subtraction angiography (DSA) images by fusing several sources of knowledge. After a brief description of a priori knowledge that the expert has provided about cerebral aneurysm, we propose a system architecture including fuzzy modeling and data fusion.

A based bayesian wavelet thresholding method to enhance nuclear imaging.

Nuclear images are very often used to study the functionality of some organs. Unfortunately, these images have bad contrast, a weak resolution, and present fluctuations due to the radioactivity disintegration. To enhance their quality, physicians have to increase the quantity of the injected radioactive material and the acquisition time.