Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Nahiduzzaman, Md.; Islam, Md. Robiul; Islam, S. M. Riazul; Goni, Md. Omaer Faruq; Anower, Md. Shamim; Kwak, Kyung Sup

doi:10.1109/access.2021.3125791

Cited by 53 publications

(21 citation statements)

References 38 publications

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“…Various neural network-based approaches have already been developed for detecting diseases from different types of medical images ( Islam et al, 2022 , Nahiduzzaman, Islam et al, 2021 ). Rajpurkar et al (2017) used deep learning on the ChestX-ray14 dataset and developed a model called CheXNet, which contained 121 layers.…”

Section: Literature Reviewmentioning

confidence: 99%

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Nahiduzzaman

Islam

Hassan

2023

Expert Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Nahiduzzaman

Islam

Hassan

2023

Expert Systems with Applications

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“…And the weight between the hidden and output layer is (None, 1) 0 calculated analytically using the Moore-Penrose Pseudoinverse method. The ELM architecture is simple and does not have iterative parameter tuning that makes the training process faster and achieves adequate performance in disease classification [42], [43]. Fig.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

et al. 2023

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Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests. However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed. The parasite inflator acts as the shape increaser of the small, darker malaria parasites in the RBC images in order to detect malaria easily. CNN-DELM has achieved a sanguine result on every performance standard compared to CNN and CNN-ELM. The proposed CNN-DELM method has achieved 97.79% and 99.66% accuracy for the original version and the modified version, respectively. Hence, the proposed CNN-DELM model has also produced either comparable or better results when compared to other methods proposed in the literature, showing its robustness in detecting malaria.

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“…A technique to identify DR was introduced by Nahiduzzaman et al [25] for binary and multiclass classification. First, Ben Graham's method has been used to pre-process DR pictures.…”

Section: Related Workmentioning

confidence: 99%

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Bapatla¹,

Harikiran²

2023

Computer Systems Science and Engineering

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Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and timeconsuming. To address the issue, a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase the performance of the classifier, the unique features are selected from the extracted feature set using Aquila Optimizer (AO) algorithm. Finally, the LightGBM model is applied to classify the input image based on the severity. Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets (MESSIDOR, APTOS, and IDRiD) using several performance metrics such as accuracy, precision, recall, and f1-score. The proposed classifier achieves 99.29%, 99.35%, and 99.31% accuracy for these three datasets respectively. The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

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Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Cited by 53 publications

References 38 publications

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine Algorithm With CNN Feature Extraction and Parasite Inflator

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

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