Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

COVID‐19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large‐scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT‐PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID‐19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID‐19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android‐based app can be deployed using MobileCapsNet model to detect COVID‐19 as it is a lightweight model and best suited for handheld devices like a mobile.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ima.22706

A lightweight capsule network architecture for detection of COVID‐19 from lung CT scans

Background and Objectives: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagnosis process is necessary. Materials and Methods: For this purpose, we developed an ensemble method that fused two deep learning models, one based on convolutional neural network and the other based on transfer learning. For the first model, called 5-CNN, we developed an efficient end-to-end trained model with five convolutional layers, while for the second model, the pre-trained VGG-19 architecture was repurposed, optimized and trained. We trained and validated our models using a dataset of ultrasound images consisting of four types of thyroidal images: autoimmune, nodular, micro-nodular, and normal. Results: Excellent results were obtained by the ensemble CNN-VGG method, which outperformed the 5-CNN and VGG-19 models: 97.35% for the overall test accuracy with an overall specificity of 98.43%, sensitivity of 95.75%, positive and negative predictive value of 95.41%, and 98.05%. The micro average areas under each receiver operating characteristic curves was 0.96. The results were also validated by two physicians: an endocrinologist and a pediatrician. Conclusions: We proposed a new deep learning study for classifying ultrasound thyroidal images to assist physicians in the diagnosis process.

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Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease. 

/pdf/generalizable-deep-learning-model-for-early-alzheimers-3ho7ijej.pdf

Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

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Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22544

A deep learning model for mass screening of COVID‐19

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

/pdf/applying-deep-learning-based-multi-modal-for-detection-of-4y2wfzzkab.pdf

Applying deep learning-based multi-modal for detection of coronavirus.

The increase in use of ICT tools and decrease in physical activities has increased the risk of disorders such as diabetes, hypertension, myopia, hypermetropia, etc. These disorders make the person more prone to eye disease such as glaucoma. The actual causes of glaucoma are still unknown. But the study of medical literature reveals that the factors such as intraocular pressure, thyroid, diabetics, eye injuries, eye surgeries, ethnic background, and myopia makes the person more prone to glaucoma. The difficulty in early detection make it an invisible thief of sight. Therefore, it is the demand of the day to design a system for its early detection. The aim of this chapter is to develop a convolutional neural network model “GlaucomaDetector” for detection of glaucoma at an early stage. The evaluation of the model on the publicly available dataset reports the accuracy of 99% for prediction of glaucoma from the input images of retina. This may prove a useful tool for doctors for quick prediction of glaucoma at an early stage. Thus, it can minimize the risk of blindness in patients.

Glaucoma Detection Using Convolutional Neural Networks


 Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientists are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning model for finding the degree of similarity of the genome of the Severe Acute Respiratory Syndrome-Coronavirus 2 (‘SARS-CoV-2’) with a given genome. This research also aims at detecting the genome of ‘SARS-CoV-2’ in the host human beings. The experimental results on the dataset publicly available at National Centre for Biotechnology Information, show that the model is effective in predicting the similarity score of the genomic sequence of ‘SARS-CoV-2’ and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus, and Human T- cell Leukaemia Virus. This is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27%. It may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.

/pdf/applying-deep-learning-for-genome-detection-of-coronavirus-55zw9tljna.pdf

Applying Deep Learning for Genome Detection of Coronavirus

As population grows, there is an exponential increase in garbage generated. This garbage contains a high percentage of plastic that is recyclable. Therefore, it is necessary to segregate different types of garbage. To minimize the impact of incorrect garbage separation, we propose using an automated system to correct the waste classification. We studied in-depth learning techniques by using comparison-based experiments. A ResNet-152 and ResNet-50 were investigated to obtain the best-fit classification model for garbage classification. The model was trained on two datasets, TrashNet and local datasets which contains 5,326 images of four garbage categories (metal, pet, plastic, and trash). The accuracies for the pre-training hyperparameters with classification time on average were 98.99% with 0.27 s and 98.81% with 0.72 s for ResNet-152 and ResNet-50, respectively. The experiment testing results on both ResNet-152 and ResNet-50 yielded outperformed accuracy respectively by 95.72% and 94.15%. Using computer vision and IoT-based system, we implemented the selected model having more steady learning rate, ResNet-50, on the IoT devices to simulate the automated garbage segregation machine. The Raspberry Pi camera connected on a microcontroller captures one by one image of the garbage when the motion sensor triggers it. The captured image is then sent to the classification model by returning a predicted garbage class displayed on the output screen. Our future works based on the output generating a garbage category. The final classification model would be integrated with a fully automated sorting machine by automatically moving to its respective bin using a motorized sliding tray.

Meet Ganpatlal Oza

Papers

A deep learning model for mass screening of COVID‐19

Applying deep learning-based multi-modal for detection of coronavirus.

Glaucoma Detection Using Convolutional Neural Networks

Applying Deep Learning for Genome Detection of Coronavirus

Comparative Study Considering Garbage Classification Using In-Depth Learning Techniques