xAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images

Ikechukwu, Agughasi Victor; Murali, S.

doi:10.1109/icdds59137.2023.10434619

Cited by 1 publication

(1 citation statement)

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“…The success of ensemble approaches and CNN-based detectors in achieving high accuracy and precision further attests to the potent capabilities of DL in transforming DR diagnostic processes. Ikechukwu and S. Murali [27] favoured the classification of normal and abnormal patients using fundus images. To reconstruct blood vessels from images, maximal primary curvature was used.…”

Section: Deep Learning Approaches For Dr Diagnosismentioning

confidence: 99%

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Bhoopal,

Rao,

Krishnappa

2024

IJEECS

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Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.

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Section: Deep Learning Approaches For Dr Diagnosismentioning

confidence: 99%