Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Lin, Chun‐Ling; Wu, Kun-Chi

doi:10.21203/rs.3.rs-2200376/v1

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…The open-source model is available on GitHub. 5 The model served to take 100 × 3D ECG tensors per patient that are individually classified by the model to generate the averaged class probability score for the per-patient prediction, with a threshold arbitrarily set to 0.5.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms

Barker,

Li,

Kotb

et al. 2024

European Heart Journal - Digital Health

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Aim European and American clinical guidelines for implantable cardioverter defibrillators (ICDs) are insufficiently accurate for ventricular arrhythmia (VA) risk stratification leading to significant morbidity and mortality. Artificial intelligence offers novel risk stratification lens through which VA capability can be determined from the electrocardiogram in normal cardiac rhythm. The aim was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory electrocardiograms. Methods A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50 based deep neural network. VA-ResNet-50 was designed to read pyramid samples of 3-lead 24-hour ambulatory electrocardiograms to decide if a heart is capable of VA based on the electrocardiogram alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory electrocardiograms as part of their NHS care between 2014 and 2022 were recruited and compared to all comer ambulatory electrograms without VA. Results Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the electrocardiogram and VA was 1.6 years (⅓ ambulatory electrocardiogram before VA). The deep neural network was able to classify electrocardiograms for VA capability with an accuracy of 0.76 (CI 95% 0.66 - 0.87), F1 score of 0.79 (0.67 - 0.90), AUC of 0.8 (0.67 - 0.91) and RR of 2.87 (1.41 - 5.81). Conclusion Ambulatory electrocardiograms confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory electrocardiograms is hypothesised to capture autonomic activity. We encourage groups to build on this open-source model.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms

Barker,

Li,

Kotb

et al. 2024

European Heart Journal - Digital Health

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“…First, each layer has the same number of filters regardless of the size of the output feature map. Second, it has twice as many filters to maintain the time complexity of each layer even if the size of the feature map is cut in half [16]. The Resnet-50 architecture is a convolutional neural network (CNN) design that includes a new idea known as shortcut connections.…”

Section: Model Sectionmentioning

confidence: 99%

AutomatedFace Mask Detection Using Pretrained CNN

Saad Al-Mukhta

2023

ANJS

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In recent times, the use of face masks has emerged as a critical subject. Automated facial mask detection can curb the transmission of the COVID-19 virus and SARS-VIRUS within communal areas by identifying individuals who are not utilizing masks. In this work, a pretrained Convolutional Neural Network (CNN) with model ResNet-50, which is initially trained on the Image Net competition data, is utilized. This model is augmented with a 300-linear layer network and fine-tuned on a dataset that comprises 1,000 facial images. During the evaluation of the validation dataset consisting of approximately 800 face images, the model achieved an impressive 99% accuracy. Its primary objective is to discover if an individual is wearing a facial mask using a cropped image of their face. By leveraging such advanced technologies, we can contribute significantly to public health and safety measures in the ongoing battle against COVID-19 and SARS-VIRUS.Keywords:CNNCOVID-19Face Mask Detection (FMD) SARSDOI: 10.22401/ANJS.26.4.12*corresponding author email: farah.saad@nahrainuniv.edu.iqThis work is licensed under a Creative Commons Attribution 4.0 International License1.Introduction COVID-19 (CO ronaVIrus Disease of 2019) and SARS(Severe Acute Respiratory Syndrome) are two viral respiratory illnesses caused by coronaviruses, both of which have had important global impacts on public health [1]. These contagious diseases emerged in different time frames but share similarities in their mode of transmission and clinical presentation, prompting the search for effective non-pharmaceutical strategies to mitigate their spread [2]. One essential strategy to reduce the transmission of respiratory droplets, which can carry the viruses, is the widespread adoption of face masks in public spaces. However, monitoring and ensuring compliance with mask-wearing practices in crowded areas can be challenging, particularly in densely populated regions. Automatic face mask detection (FMD) systems powered by advanced technologies offer a potential solution to this challenge [3]. Convolutional Neural Networks (CNNs) and deep learning techniques have demonstrated remarkable success in computer vision tasks, including image classification [4]. In this context, utilizing pretrained CNN models, such as ResNet-50, has shown promising results for identifying faces wearing or not wearing masks. In this work, we explore applying a pretrained ResNet-50 CNN model fine-tuned on a well-balanced dataset of 12,000cropped photos of faces with and without masks. The goal is to create an accurate and reliable FMD (Face Mask Detection) system that can automatically identify individuals who are not wearing masks in public spaces. Through this work, we aim to contribute to the expanding realm of research on FMD as a crucial tool to prevent the spread of respiratory illnesses. The successful implementation of such technology has the ability to play a significant role in safeguarding public health, improving safety measures, and aiding in the ongoing battle against COVID-19 and SARS in various community settings.

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“…To this end, the automatic retinal fundus photography strategy [5,6] using deep neural networks becomes popular, which can help doctors to screen glaucoma. As shown in Figure 1, the retinal fundus image shoots the main structure of the fundus including the optic disc (OD) and optic cup (OC).…”

Section: Introductionmentioning

confidence: 99%

Self-supervised Pre-training for Joint Optic Disc and Cup Segmentation via Attention-Aware Network

Zhou,

Zheng,

Zhou

et al. 2023

Preprint

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Image segmentation is a fundamental task in deep learning, which is able to analyse the essence of the images for further development. However, for the supervised learning segmentation method, collecting pixel-level labels is very time-consuming and labour-intensive. In the medical image processing area for optic disc and cup segmentation, we consider there are two challenging problems that remain unsolved. One is how to design an efficient network to capture the global field of the medical image and execute fast in real applications. The other is how to train the deep segmentation network using a few training data due to some medical privacy issues. In this paper, to conquer such issues, we first design a novel attention-aware segmentation model equipped with the multi-scale attention module in the pyramid structure-like encoder-decoder network, which can efficiently learn the global semantics and the long-range dependencies of the input images. Furthermore, we also inject the prior knowledge that the optic cup lies inside the optic disc by a novel loss function. Then, we propose a self-supervised contrastive learning method for optic disc and cup segmentation. The unsupervised feature representation is learned by matching an encoded query to a dictionary of encoded keys using a contrastive technique. Finetuning the pre-trained model using the proposed loss function can help achieve good performance for the task. To validate the effectiveness of the proposed method, extensive systemic evaluations on different public challenging optic disc and cup benchmarks including DRISHTI-GS and REFUGE datasets demonstrate the superiority of the proposed method, which can achieve new state-of-the-art performance. The code will be made publicly available.

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Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Cited by 4 publications

References 18 publications

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms

AutomatedFace Mask Detection Using Pretrained CNN

Self-supervised Pre-training for Joint Optic Disc and Cup Segmentation via Attention-Aware Network

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