Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

ADPT: An Automated Disease Prognosis Tool Towards Classifying Medical Disease Using Hybrid Architecture of Deep Learning Paradigm

The Covid 19 beta coronavirus, commonly known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the most significant RNA-type viruses in human health. However, more such epidemics occurred beforehand because they were not limited. Much research has recently been carried out on classifying the disease. Still, no automated diagnostic tools have been developed to identify multiple diseases using X-ray, Computed Tomography (CT) scan, or Magnetic Resonance Imaging (MRI) images. In this research, several Tate-of-the-art techniques have been applied to the Chest-Xray, CT scan, and MRI segmented images' datasets and trained them simultaneously. Deep learning models based on VGG16, VGG19, InceptionV3, ResNet50, Capsule Network, DenseNet architecture, Exception and Optimized Convolutional Neural Network (Optimized CNN) were applied to the detecting of Covid-19 contaminated situation, Alzheimer's disease, and Lung infected tissues. Due to efforts taken to reduce model losses and overfitting, the models' performances have improved in terms of accuracy. With the use of image augmentation techniques like flip-up, flip-down, flip-left, flip-right, etc., the size of the training dataset was further increased. In addition, we have proposed a mobile application by integrating a deep learning model to make the diagnosis faster. Eventually, we applied the Image fusion technique to analyze the medical images by extracting meaningful insights from the multimodal imaging modalities. © 2022 IEEE.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

An Approach to Detect COVID-19 Disease from CT Scan Images using CNN - VGG16 Model

As a result of the outbreak, an unusual virus spread event has occurred, threatening human safety worldwide. To prevent infections from spreading quickly, large numbers of people must be screened. Rapid Test and RT-PCR are common testing tool for regular testing that is used to test all covid affected users. However, the increasing number of false positives has paved the way for the investigation of alternative test methods for corona virus effected patients' chest X-rays have shown to be an effective alternate predictor for testing if an individual is affected with COVID-19 virus. However, consistency is, once again, dependent on radiological experience. A diagnostic decision support device that assists the physician in evaluating the victims' lung scans can alleviate the doctor's medical workload. Machine Learning Techniques, specifically Convolutional Neural Networks (CNN) VGG16 model is used to train dataset and use trained model to predict, have been developed in this project. Four distinct deep CNN architectures are tested on photographs of chest X-rays for treatment of COVID-19. The collection of data sets of covid 19 X-ray imageries and non-covid 19 X-ray imageries are used to train the model and test its accuracy. CNN-based architectures were discovered to be capable of diagnosing COVID-19 disease. © 2022 IEEE.

COVID-19 Detection Model on Chest CT Scan and X-ray Images Using VGG16 Convolutional Neural Network

In this pandemic of COVID-19, many people's lives are highly affected in various kinds of aspects. Tests are conducted due to the rising number of infected people, with the PCR test as the current gold standard for many. However, many experts consider the PCR test inaccurate due to the resulting false negative and false positive test results. In order to solve the problem, through this paper, the use of a deep learning model is proposed based on a customized VGG16 CNN as a way to identify the presence COVID-19 virus. The biomarkers used in this paper are X-ray and CT scan images of the lungs. At the end of the research, it can be concluded that both CT scan and X-ray images can be used to detect COVID-19 by using VGG16. However, by comparing the performance of the proposed X-ray and CT scan biomarker-based models, it can be inferred that the X-ray biomarker-based model obtained a higher accuracy score of 97% compared to the CT scan-based model with 93% accuracy. This research proved that the X-ray model got a better score and is a better alternative than CT scan, although both have potential and can be considered accurate alternatives to the PCR tests. © 2021 IEEE.