Method For Hemorrhage Detection Research Articles

Deep learning-based hemorrhage detection for diabetic retinopathy screening

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabetic retinopathy in the early phase. Diagnosing the disease in its initial stage is crucial to adopt proper treatment so the repercussions can be prevented. The automatic deep learning-based hemorrhage detection method is proposed that can be used as the second interpreter for ophthalmologists to reduce the time and complexity of conventional screening methods. The quality of the images was enhanced, and the prospective hemorrhage locations were estimated in the preprocessing stage. Modified gamma correction adaptively illuminates fundus images by using gradient information to address the nonuniform brightness levels of images. The algorithm estimated the locations of potential candidates by using a Gaussian match filter, entropy thresholding, and mathematical morphology. The required objects were segmented using the regional diversity at estimated locations. The novel hemorrhage network is propounded for hemorrhage classification and compared with the renowned deep models. Two datasets benchmarked the model’s performance using sensitivity, specificity, precision, and accuracy metrics. Despite being the shallowest network, the proposed network marked competitive results than LeNet-5, AlexNet, ResNet50, and VGG-16. The hemorrhage network was assessed using training time and classification accuracy through synthetic experimentation. Results showed promising accuracy in the classification stage while significantly reducing training time. The research concluded that increasing deep network layers does not guarantee good results but rather increases training time. The suitable architecture of a deep model and its appropriate parameters are critical for obtaining excellent outcomes.

Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages.

Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares the classification of conventional and deep features. Especially, the method identifies hemorrhage connected with blood vessels or residing at the retinal border and was reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and morphological operation. Hemorrhages are segmented by a novel technique based on the regional variance of intensities. Features are then extracted by conventional methods and deep models for training support vector machines and the results are evaluated. Evaluation metrics for each model are promising, but findings suggest that comparatively, deep models are more effective than conventional features.

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method.

Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial feasibility study, we collected ultrasound color Doppler images of femoral arteries in an animal model of vascular injury (N = 11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.

Combined Planar Magnetic Induction Tomography for Local Detection of Intracranial Hemorrhage

Magnetic induction tomography (MIT) is a contactless and harmless technique for intracranial hemorrhage detection. Due to the low electrical conductivity of biological tissues, the phase shift signals are very weak. To improve the sensitivity of the phase shift signals and achieve the local detection of intracranial hemorrhage, a planar MIT method is proposed. The coil parameters were optimized based on the sensitivity analysis. The performance of single planar and combined planar MITs was investigated with a 3-D anatomical head model. Two evaluation indexes, correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {cc}_{\text {ima}}$ </tex-math></inline-formula> and localization error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{e}$ </tex-math></inline-formula> , were introduced to evaluate the imaging quality. Finally, experimental tests were carried out to verify the ability of combined planar MIT for hemorrhage detection. The simulation results indicate that the combined planar MIT has better imaging quality and distinguishing ability for the hemorrhage with different deviation positions, depths, and volumes. For the 8-mL hemorrhage, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {cc}_{\text {ima}}$ </tex-math></inline-formula> of combined planar MIT is increased by 40.2%, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{e}$ </tex-math></inline-formula> is reduced by 77.8% compared with single planar MIT. The experimental results indicate that a 16-mL hemorrhage can be reconstructed, and the change in position can be distinguished by combined planar MIT. The study demonstrates that combined planar MIT is a feasible method for hemorrhage detection. The proposed method provides a portable solution for local detection of intracranial hemorrhage and promotes the practical application of MIT in biomedical imaging.

Retinal hemorrhage detection by rule-based and machine learning approach.

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

Detection of Hemorrhage from Fundus Images using Hybrid Method

retinopathy is serious health issue in recent survey. Diabetic disease occurs when insulin level in blood vessels decreases and body is unable to process properly. if diabetic increases then it effect the retina of fundus.and this disease damage the blood vessels and damage the retina. The aim of this study to detect blood vessels, identify hemorrhage and classify diabetic retinopathy in normal,moderate and proliferative stage. There are two levels of diabetic retinopathy non proliferative and proliferative stage.non proliferative is first stage of diabetic retinopathy which include micro aneurysms and cotton wool spots. if it is increases then it enters into proliferative stage. In past days hemorrhage detected manually pixel by pixel method. In this paper developed automated method for detection of hemorrhage. in which used template matching technique for the detection of hemorrhage. For detection of proper size of hemorrhage region growing segmentation used in this method. for elimination of false positive fovea filter used this will eliminate wrongly detected blood vessels as hemorrhage. This study is improving our automated hemorrhage detection method to help diagnosis of diabetic retinopathy. The sensitivity for the detection of abnormal cases were 80 % and 90% respectively. These results indicate that this new method may improve the performance of diagnosis system for detection of hemorrhage.

English

2 ABSTRACT: Image processing of a fundus image is performed for the early recognition of diabetic retinopathy. Diabetic retinopathy classified in two stages. First is non-proliferative which includes microanearism and when blood vessels are leak and it comes into proliferative stage which is called hemorrhage. In this paper I discussed about detection of hemorrhage in diabetic retinopathy. extraction of hemorrhages using template matching with templates of various shapes has not been reported.Microanuarisem are early sign of diabetic retinopathy Detection of hemorrhage may help the clinician in the diagnosis of diabetic retinopathy and might be a useful tool for early detection of diabetic retinopathy progression especially in the telemedicine. Diabetic retinopathy is a serious complication of diabetes mellitus and a major cause of blindness worldwide. Signs of diabetic retinopathy include red lesions such as micro aneurysms (MA), intraregional hemorrhages, and bright lesions, such as exudates and cotton wool spots. So early detection and proper treatment can prevent our eyes from blindness. In this paper I discussed hybrid method and brightness correction method for detection of hemorrhage in diabetic retinopathy. There was using various shape of template for detection of hemorrhage. Second method I applied in this study it was brightness correction and clahe method for detection. For recognize exact shape of hemorrhage region growing segmentation was used. Diabetic retinopathy is the most common cause of blindness in the working population of the United States. Early diagnosis and timely treatment have been preventing visual loss and blindness in patients with diabetes.