Lesion-aware attention with neural support vector machine for retinopathy diagnosis

Shaik, Nagur Shareef; Cherukuri, Teja Krishna

doi:10.1007/s00138-021-01253-y

Cited by 25 publications

(2 citation statements)

References 41 publications

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“…Numerous research works have been published in this field. Recent advancements in the field of computer vision have paved the way for the application of deep learning and convolutional neural networks in the field of medical image diagnosis like retinopathy identification [ 14 – 16 ], tumor detection [ 17 – 19 ], and also for COVID-19 detection [ 20 ]. In this section, we discuss several recent research works done in the field of COVID-19 detection using chest X-rays and CT scans.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach

Kumar

Mallik

2022

Neural Process Lett

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The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread of the disease. In this paper, we propose a novel Trained Output-based Transfer Learning (TOTL) approach for COVID-19 detection from chest X-rays. We start by preprocessing the Chest X-rays of the patients with techniques like denoising, contrasting, segmentation. These processed images are then fed to several pre-trained transfer learning models like InceptionV3, InceptionResNetV2, Xception, MobileNet, ResNet50, ResNet50V2, VGG16, and VGG19. We fine-tune these models on the processed chest X-rays. Then we further train the outputs of these models using a deep neural network architecture to achieve enhanced performance and aggregate the capabilities of each of them. The proposed model has been tested on four recent COVID-19 chest X-rays datasets by computing several popular evaluation metrics. The performance of our model has also been compared with various deep transfer learning models and several contemporary COVID-19 detection methods. The obtained results demonstrate the efficiency and efficacy of our proposed model.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach

Kumar

Mallik

2022

Neural Process Lett

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“…Due to the present COVID-19 epidemic, several efforts are being made to identify and combat it. Recent advancements in computer vision have paved the way for applying deep learning and convolutional neural networks to medical image diagnosis, such as retinopathy identification [21][22][23][24][25] and tumor discovery [26][27][28]. Numerous research studies have been published in this field to combat COVID-19.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection

et al. 2022

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In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.

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Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

Goswami

Ashwini

Dash

2023

Communications in Computer and Information Science

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Lesion-aware attention with neural support vector machine for retinopathy diagnosis

Cited by 25 publications

References 41 publications

COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach

COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach

A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection

Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

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