Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification

Dondeti, Venkatesulu; Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu

doi:10.18280/ria.340308

Cited by 28 publications

(10 citation statements)

References 26 publications

(26 reference statements)

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“…Transfer Learning is a prominent Deep Learning approach in which the knowledge gained while training that model using large scale datasets can be transferred to another model specifically designed to solve a similar or related problem [ 17 , 59 ]. Transfer learning strategies often use pre-trained models, which are deep neural network models trained on huge labeled data and whose weights are readily available for usage [ 47 ].…”

Section: Background Studymentioning

confidence: 99%

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Shaik

Cherukuri

2022

Computers in Biology and Medicine

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Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.

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Section: Background Studymentioning

confidence: 99%

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Shaik

Cherukuri

2022

Computers in Biology and Medicine

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“…where and are the mean of all values of the matri Equation (1) always results in a value within 0 and 1; 0 and 1 if they are the same. Since we are working with calculate the coefficients separately for three different cha three channels are 1, then we can conclude that the second -SVM) for DR classification [41]. They extracted deep features using the Neural Architecture Search Network (NASNet) architecture.…”

Section: Dealing With Duplicate Imagesmentioning

confidence: 99%

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

et al. 2021

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Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

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“…To compare and correlate its performance with the top layer of the following DL architectures (CNN, ReLU, Sigmoid, Softmax Regression function), on analysis and Transfer Learning approach [5][6][7] the lack of a data set for medical imagery is great.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

Naralasetti¹,

Shaik²,

Katepalli³

et al. 2021

Self Cite

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Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on these datasets indicate that the proposed model is efficient. The suggested CNN architecture with specified hyper parameters allows the model to outperform several existing models by achieving accuracy of 97.73% and 91.17% respectively for binary and multi-class classification tasks of pneumonia disease.

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Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification

Cited by 28 publications

References 26 publications

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

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