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. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

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. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.

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. The application of diagnostic tools based on Machine Learning (ML) comes into play here as they are capable of distinguishing between HC subjects and PD patients within an SWEDD group. In the present study, three ML algorithms were used to separate PD patients from HC within an SWEDD group. Data of 548 subjects were firstly analyzed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. Using the best reduction technique result, we built the following clustering models: Density-Based Spatial (DBSCAN), K-means and Hierarchical Clustering. According to our findings, LDA performs better than PCA; therefore, LDA was used as input for the clustering models. The different models' performances were assessed by comparing the clustering algorithms outcomes with the ground truth after a follow-up. Hierarchical Clustering surpassed DBSCAN and K-means algorithms by 64%, 78.13% and 38.89% in terms of accuracy, sensitivity and specificity. The proposed method demonstrated the suitability of ML models to distinguish PD patients from HC subjects within an SWEDD group.

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. In total, 120 eyes underwent optical coherence tomography (OCT) and inverted flap ILM peeling for surgery. Then 510 B scan of macular OCT was acquired 9 months after surgery. MH diameter, basal MH diameter (b), nasal and temporal arm lengths and macular hole angle were measured. Indices including hole form factor, MH index, diameter hole index (DHI) and tractional hole, MH area index and MH volume index were calculated. Receiver operating characteristic (ROC) curves and cut­off values were derived for each indices predicting closure or not of the MH. The area under the receiver operating characteristic curve (AUC) and kappa value were calculated to evaluate performance of the medical decision support system (MDSS) in predicting the MH closure. From the ROC curve analysis, it was derived that MH indices like MH diameter, diameter hole index (DHI), MH index, and hole formation factor were capable of successfully predicting MH closure while basal diameter, DHI and MH area index predicted none closure MH. The MDSS achieved an AUC of 0.984 with a kappa value of 0.934. Based on the preoperative OCT parameters, our ML model achieved remarkable accuracy in predicting MH outcomes after pars plana vitrectomy and inverted flap ILM peeling. Therefore, MDSS may help optimize surgical planning for full thickness macular hole patients in the future.

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. METHODS: Convolutional neural network (CNN) is considered as the leading model in deep learning and is highly recommended for the design of computer-aided diagnosis systems thanks to its promising results on medical image analysis. The proposed BCNN scheme consists of two-stream CNNs (VGG16 and VGG19) as feature extractors followed by a support vector machine (SVM) classifier for false positive reduction. Series of experiments are performed by introducing the bilinear vector features extracted from three BCNN combinations into various types of SVMs that we adopted instead of the original softmax to determine the most suitable classifier for our study. RESULTS: The method performance was evaluated on 3186 images from the public LUNA16 database. We found that the BCNN [VGG16, VGG19] combination with and without SVM surpassed the [VGG16]2 and [VGG19]2 architectures, achieved an accuracy rate of 91.99% against 91.84% and 90.58%, respectively, and an area under the curve (AUC) rate of 95.9% against 94.8% and 94%, respectively. CONCLUSION: The proposed method improved the outcomes of conventional CNN-based architectures and showed promising and satisfying results, compared to other works, with an affordable complexity. We believe that the proposed BCNN can be used as an assessment tool for radiologists to make a precise analysis of lung nodules and an early diagnosis of lung cancers.

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. OBJECTIVE: Several CNN-based research projects have been initiated to design robust and efficient CAD schemes for the detection and classification of lung nodules. This paper reviews the recent works in this area and gives an insight into technical progress. METHODS: First, a brief overview of CNN models and their basic structures is presented in this investigation. Then, we provide an analytic comparison of the existing approaches to discover recent trend and upcoming challenges. We also introduce an objective description of both handcrafted and deep learning features, as well as the types of nodules, the medical imaging modalities, the widely used databases, and related works in the last three years. The articles presented in this work were selected from various databases. About 57% of reviewed articles published in the last year. RESULTS: Our analysis reveals that several methods achieved promising performance with high sensitivity rates ranging from 66% to 100% under the false-positive rates ranging from 1 to 15 per CT scan. It can be noted that CNN models have contributed to the accurate detection and early diagnosis of lung nodules. CONCLUSIONS: From the critical discussion and an outline for prospective directions, this survey provide researchers valuable information to master the deep learning concepts and to deepen their knowledge of the trend and latest techniques in developing 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. METHODS: Here, the designed framework takes DSA images including IA as input and produces images where the IA is clearly identified and localized. The multi-step approach is defined as the following: The first phase presents the determination of initial aneurysm candidates using the uniform local binary patterns (LBPs). The LBPs are calculated from these images in order to identify texture contents of both aneurysm and no-aneurysm classes. The second phase presents the false positives removal using a shape descriptor based on contours: the GFD. RESULTS: We demonstrated that the proposed detection method successfully recognized morphological features of intracranial aneurysm. The results demonstrated excellent agreement between manual and automated detections. With the computerized IA detection framework, all aneurysms were correctly detected with zero false negative and low FP rates. CONCLUSION: This study shows the potential of LBP and GFD as a feature descriptors and paves the way for a whole image analysis tool to predict intracranial aneurysm risk of rupture.

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. The results on the studied cases are very promising.

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. In this paper, we propose an alternative solution. It consists in a software framework that enhances nuclear image quality and reduces statistical fluctuations. Since these images are modeled as the realization of a Poisson process, we propose a new framework that performs variance stabilizing of the Poisson process before applying an adapted Bayesian wavelet shrinkage. The proposed method has been applied on real images, and it has proved its performance.