H-Deep-Net: A deep hybrid network with stationary wavelet packet transforms for Retinal detachment classification through fundus images.

Nowadays, automated disease diagnosis has become a vital role in the medical field due to the significant population expansion. An automated disease diagnostic approach assists clinicians in the diagnosis of disease by giving exact, consistent, and prompt results, along with minimizing the mortality rate. Retinal detachment has recently emerged as one of the most severe and acute ocular illnesses, spreading worldwide. Therefore, an automated and quickest diagnostic model should be implemented to diagnose retinal detachment at an early stage. This paper introduces a new hybrid approach of best basis stationary wavelet packet transform and modified VGG19-Bidirectional long short-term memory to detect retinal detachment using retinal fundus images automatically. In this paper, the best basis stationary wavelet packet transform is utilized for image analysis, modified VGG19-Bidirectional long short-term memory is employed as the deep feature extractors, and then obtained features are classified through the Adaptive boosting technique. The experimental outcomes demonstrate that our proposed method obtained 99.67% sensitivity, 95.95% specificity, 98.21% accuracy, 97.43% precision, 98.54% F1-score, and 0.9985 AUC. The model obtained the intended results on the presently accessible database, which may be enhanced further when additional RD images become accessible. The proposed approach aids ophthalmologists in identifying and easily treating RD patients.

Multimodal Neuroimaging based Alzheimer's Disease Diagnosis using Evolutionary RVFL Classifier.

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based Morphometry.

Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model.

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis.

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features.

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images.

Multi-COVID-Net: Multi-objective optimized network for COVID-19 diagnosis from chest X-ray images.

Coronavirus Disease 2019 (COVID-19) had already spread worldwide, and healthcare services have become limited in many countries. Efficient screening of hospitalized individuals is vital in the struggle toward COVID-19 through chest radiography, which is one of the important assessment strategies. This allows researchers to understand medical information in terms of chest X-ray (CXR) images and evaluate relevant irregularities, which may result in a fully automated identification of the disease. Due to the rapid growth of cases every day, a relatively small number of COVID-19 testing kits are readily accessible in health care facilities. Thus it is imperative to define a fully automated detection method as an instant alternate treatment possibility to limit the occurrence of COVID-19 among individuals. In this paper, a two-step Deep learning (DL) architecture has been proposed for COVID-19 diagnosis using CXR. The proposed DL architecture consists of two stages, "feature extraction and classification". The "Multi-Objective Grasshopper Optimization Algorithm (MOGOA)" is presented to optimize the DL network layers; hence, these networks have named as "Multi-COVID-Net". This model classifies the Non-COVID-19, COVID-19, and pneumonia patient images automatically. The Multi-COVID-Net has been tested by utilizing the publicly available datasets, and this model provides the best performance results than other state-of-the-art methods.

WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images.

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19.

The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

A novel four-step feature selection technique for diabetic retinopathy grading.

Diabetic retinopathy is a microvascular complication of diabetes mellitus that develops over time. Diabetic retinopathy is one of the retinal disorders. Early detection of diabetic retinopathy reduces the chances of permanent vision loss. However, the identification and regular diagnosis of diabetic retinopathy is a time-consuming task and requires expert ophthalmologists and radiologists. In addition, an automatic diabetic retinopathy detection technique is necessary for real-time applications to facilitate and minimize potential human errors. Therefore, we propose an ensemble deep neural network and a novel four-step feature selection technique in this paper. In the first step, the preprocessed entropy images improve the quality of the retinal features. Second, the features are extracted using a deep ensemble model include InceptionV3, ResNet101, and Vgg19 from the retinal fundus images. Then, these features are combined to create an ample feature space. To reduce the feature space, we propose four-step feature selection techniques: minimum redundancy, maximum relevance, Chi-Square, ReliefF, and F test for selecting efficient features. Further, appropriate features are chosen from the majority voting techniques to reduce the computational complexity. Finally, the standard machine learning classifier, support vector machines, is used in diabetic retinopathy classification. The proposed method is tested on Kaggle, MESSIDOR-2, and IDRiD databases, available publicly. The proposed algorithm provided an accuracy of 97.78%, a sensitivity of 97.6%, and a specificity of 99.3%, using top 300 features, which are better than other state-of-the-art methods.

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network.

The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.

BHARAT: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Progressive and relentless efforts are being made for therapeutic development by way of advancing understanding of non-invasive imaging modalities for the causal molecular process of AD. We present a Hadoop-based big data framework integrating non-invasive magnetic resonance imaging (MRI), MR spectroscopy (MRS) as well as neuropsychological test outcomes to identify early diagnostic biomarkers of AD. This big data framework for AD incorporates the three "V"s (volume, variety, velocity) with advanced data mining, machine learning, and statistical modeling algorithms. A large volume of longitudinal information from non-invasive imaging modalities with colligated parametric variety and speed for both data acquisition and processing as velocity complete the fundamental requirements of this big data framework for early AD diagnosis. Brain structural, neurochemical, and behavioral features are extracted from MRI, MRS, and neuropsychological scores, respectively. Subsequently, feature selection and ensemble-based classification are proposed and their outputs are fused based on the combination rule for final accurate classification and validation from clinicians. A multi-modality-based decision framework (BHARAT) for classification of early AD will be immensely helpful.