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Inflammatory Response II: Inflammatory Exudate and Tissue Repair01:24

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The immune system's inflammatory response destroys the invading pathogen, permitting the tissue to heal. The changes during the cellular and vascular stages allow exudate formation at the site of inflammation. The inflammatory exudate released from the wound has high protein content and a specific gravity above 1.020.
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Patients with esophageal strictures often experience a range of symptoms. Initially, they may have difficulty swallowing solid foods, which can progress to include liquids. Additional symptoms may involve chest pain or discomfort, regurgitating food and fluids, heartburn, unintentional weight loss, coughing or choking during meals, and hoarseness.
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Endocarditis can present various clinical features depending on the causative organism and the patient's underlying health conditions. Initially, the clinical features of infective endocarditis develop gradually, presenting with nonspecific symptoms that can be easily mistaken for other illnesses.General SymptomsEarly symptoms of infective endocarditis are fever, chills, weakness, malaise, fatigue, and weight loss. These symptoms reflect the systemic nature of the infection and the body's...
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Exudate detection in fundus images using deeply-learnable features.

Parham Khojasteh1, Leandro Aparecido Passos Júnior2, Tiago Carvalho3

  • 1Royal Melbourne Institute of Technology, Biosignals Laboratory, School of Engineering, 124 La Trobe St, Melbourne, Australia.

Computers in Biology and Medicine
|November 16, 2018
PubMed
Summary
This summary is machine-generated.

Deep learning models, including ResNet-50, significantly improve the detection of retinal exudates, an early sign of diabetic retinopathy. This advancement enhances diagnostic accuracy for the disease.

Keywords:
Convolutional neural networksDeep learningDeep residual networksDiabetic retinopathyDiscriminative restricted Boltzmann machinesExudate detection

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy detection relies on identifying retinal exudates.
  • Current automated methods using Convolutional Neural Networks (CNNs) show limited performance.
  • Improved automated detection is crucial for timely diagnosis and management.

Purpose of the Study:

  • To investigate deep learning techniques for enhanced automatic exudate detection.
  • To maximize sensitivity and specificity in identifying retinal exudates.
  • To compare the performance of various deep learning models and classifiers.

Main Methods:

  • Comparison of deep learning methods: CNNs, ResNet-50, and Discriminative Restricted Boltzmann Machines.
  • Utilized both supervised and unsupervised classification approaches.
  • Experiments conducted on two public datasets: DIARETDB1 and e-Ophtha.

Main Results:

  • ResNet-50 combined with Support Vector Machines achieved superior performance.
  • Achieved an accuracy of 98% and a sensitivity of 0.99.
  • Demonstrated high efficacy in analyzing fundus images for exudate detection.

Conclusions:

  • ResNet-50 is a highly effective model for automated exudate detection in fundus images.
  • This approach can significantly aid in the early diagnosis of diabetic retinopathy.
  • Deep learning offers a promising avenue for improving ophthalmic diagnostic tools.