Tulsi Leaf Disease Detection using CNN

Since the start of the covid 19 pandemic, a wide range of medications have been produced and are currently being utilized to treat the disease. Tulsi, in addition to all of the chemical-based medications, is an herbal therapy that is particularly effective in the treatment of this ailment. Tulsi has been used to heal ailments and infections for millennia, particularly in India. Because we use tulsi for medicinal purposes, it's vital to monitor its health in order to reap the full benefits of its herbal properties. Plant diseases harm the health and growth of the plant. Disease detection in plants is crucial so that it can be treated before it spreads throughout the plant. To detect illnesses in tulsi leaves, we propose employing a model based on convolution neural networks. Image processing and CNN are widely employed. The prepared model extracts the image's key features and categorizes it into different disorders. The model has a 75 percent accuracy rate. © 2022 IEEE.

A Vision Transformer-based Approach for Automatic COVID-19 Diagnosis on Chest X-ray Images.

The new type of coronavirus disease (COVID-19), which has emerged in recent years, has become a serious disease that threatens health worldwide. COVID-19, which can be transmitted very quickly and with serious increases in death, has paved the way for many concerns. With the spread of the epidemic to a universal dimension, many studies have been carried out for the early diagnosis of this disease. With early diagnosis, both fatal cases are prevented and the planning of the epidemic can be easier. The fact that X-ışını images are much more advantageous than other imaging techniques in terms of time and applicability, and also that they are economical, has led to the focus of early diagnosis-based applications and methods on these images. Deep learning approaches have had a great impact in the diagnosis of COVID-19, as in the diagnosis of many diseases. In this study, we propose a diagnostic system based on the transformer method, which is the most up-to-date and much more popular architecture than previous techniques of deep learning such as CNN-based approaches. This method includes an approach based on vision transformer models and a more effective diagnosis of COVID-19 disease on a new dataset, the COVID-QU-Ex dataset. In experimental studies, it has been observed that vision transformer models are more successful than CNN models. In addition, the ViT-L16 model showed a much higher performance compared to similar studies in the literature, providing test accuracy and F1- score of over 96%. (English) [ FROM AUTHOR] Son yıllarda ortaya çıkan yeni tip Koronavirüs hastalığı (COVID-19), dünya çapında sağlığı tehdit eden ciddi bir hastalık olmuştur. COVID-19 çok hızlı bir şekilde bulaşabilen ve ciddi ölüm artışları ile birçok endişeye zemin hazırlamıştır. Salgının evrensel boyuta taşınmasıyla bu hastalığın erken teşhisine yönelik birçok çalışma yapılmıştır. Erken teşhis ile hem ölümcül vakaların önüne geçilmiş olunmakta hem de salgının planlanması daha kolay olabilmektedir. Xışını görüntülerinin zaman ve uygulanabilirlik açısından diğer görüntüleme tekniklerine nazaran çok daha avantajlı olması ve ayrıca ekonomik olması erken teşhis bazlı uygulama ve yöntemlerin bu görüntülerin üzerine yoğunlaşmasına neden olmuştur. Derin öğrenme yaklaşımları birçok hastalık teşhisinde olduğu gibi COVID-19 teşhisinde de çok büyük bir etki oluşturmuştur. Bu çalışmada, derin öğrenmenin CNN tabanlı yaklaşımları gibi daha önceki tekniklerinden ziyade en güncel ve çok daha popüler bir mimarisi olan transformatör yöntemine dayalı bir teşhis sistemi önerdik. Bu sistem, görü transformatör modelleri temelli bir yaklaşım ve yeni bir veri seti olan COVID-QU-Ex üzerinde COVID-19 hastalığının daha efektif bir teşhisini içermektedir. Deneysel çalışmalarda, görü transformatör modellerinin CNN modellerinden daha başarılı olduğu gözlemlenmiştir. Ayrıca, ViT-L16 modeli %96'nın üzerinde test doğruluğu ve F1-skoru sunarak, literatürde benzer çalışmalara kıyasla çok daha yüksek bir başarım göstermiştir. (Turkish) [ FROM AUTHOR] Copyright of Journal of the Institute of Science & Technology / Fen Bilimleri Estitüsü Dergisi is the property of Igdir University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Deep learning model for binary classification of COVID-19 based on Chest X-Ray

COVID-19 is a novel virus infecting the upper respiratory tract and lungs. On a scale of the global pandemic, the number of cases and deaths had been increasing each day. Chest X-ray (CXR) images proved effective in monitoring a variety of lung illnesses, including the COVID-19 disease. In recent years, deep learning (DL) has become one of the most significant topics in the computing world and has been extensively applied in several medical applications. In terms of automatic diagnosis of COVID-19, those approaches had proven to be very effective. In this research, a DL technology based on convolution neural networks (CNN) models had been implemented with less number of layers with tuning parameters that will take less time for training for binary classification of COVID-19 based on CXR images. Experimental results had shown that the proposed model for training had achieved an accuracy of 96.68%, Recall of 94.12%, Precision of 93.49%, Specificity of 97.61%, and F1 Score of 93.8%. Those results had shown the high value of utilizing DL for early COVID-19 diagnosis, which can be utilized as a useful tool for COVID-19 screening. © 2023 IEEE.

Magnetic Particle Spectroscopy for Point-of-Care: A Review on Recent Advances.

Since its first report in 2006, magnetic particle spectroscopy (MPS)-based biosensors have flourished over the past decade. Currently, MPS are used for a wide range of applications, such as disease diagnosis, foodborne pathogen detection, etc. In this work, different MPS platforms, such as dual-frequency and mono-frequency driving field designs, were reviewed. MPS combined with multi-functional magnetic nanoparticles (MNPs) have been extensively reported as a versatile platform for the detection of a long list of biomarkers. The surface-functionalized MNPs serve as nanoprobes that specifically bind and label target analytes from liquid samples. Herein, an analysis of the theories and mechanisms that underlie different MPS platforms, which enable the implementation of bioassays based on either volume or surface, was carried out. Furthermore, this review draws attention to some significant MPS platform applications in the biomedical and biological fields. In recent years, different kinds of MPS point-of-care (POC) devices have been reported independently by several groups in the world. Due to the high detection sensitivity, simple assay procedures and low cost per run, the MPS POC devices are expected to become more widespread in the future. In addition, the growth of telemedicine and remote monitoring has created a greater demand for POC devices, as patients are able to receive health assessments and obtain results from the comfort of their own homes. At the end of this review, we comment on the opportunities and challenges for POC devices as well as MPS devices regarding the intensely growing demand for rapid, affordable, high-sensitivity and user-friendly devices.

Machine Learning in ASD: An Intensive Study of Automated Disease Prediction System

Of late, due to drastic climate change and excessive pollution, people live in such an atmosphere where they have to combat continuously several deadly diseases. To get the proper treatment of such diseases, people must rely on appropriate diagnoses. There are a lot of signs or symptoms that bear the existence of a particular condition. Generally, almost all the people who suffer from viral infections, dengue, and COVID-19 get a common sign of high fever. Therefore, it is challenging for doctors to determine the exact disease with this particular symptom. Accordingly, a technically equipped medical system should be developed to get a more error-free diagnosis. In this context, a case study uses the Random Forest Algorithm to combine diagnostic prediction and technology, which will help medical practitioners detect diseases. Agile Software can be used here. One of the essential advantages of agile methodology is speed to market and risk reduction. This paper showcases a module developed with the help of Machine Learning. Here, Agile Software is designed to become very effective in detecting a particular disease more efficiently. In this specific system preventing errors and malfunctions has been proven to be 95% effective in the medical field. © 2023 Scrivener Publishing LLC.

Do Preprocessing and Class Imbalance Matter to the Deep Image Classifiers for COVID-19 Detection? An Explainable Analysis

In a world withstanding the waves of a raging pandemic, respiratory disease detection from chest radiological images using machine-learning approaches has never been more important for a widely accessible and prompt initial diagnosis. A standard machine-learning disease detection workflow that takes an image as input and provides a diagnosis in return usually consists of four key components, namely input preprocessor, data irregularities (like class imbalance, missing and absent features, etc.) handler, classifier, and a decision explainer for better clarity. In this study, we investigate the impact of the three primary components of the disease-detection workflow leaving only the deep image classifier. We specifically aim to validate if the deep classifiers may significantly benefit from additional preprocessing and efficient handling of data irregularities in a disease-diagnosis workflow. To elaborate, we explore the applicability of seven traditional and deep preprocessing techniques along with four class imbalance handling approaches for a deep classifier, such as ResNet-50, in the task of respiratory disease detection from chest radiological images. While deep classifiers are more capable than their traditional counterparts, explaining their decision process is a significant challenge. Therefore, we also employ three gradient visualization algorithms to explain the decision of a deep classifier to understand how well each of them can highlight the key visual features of the different respiratory diseases. © 2020 IEEE.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements

The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user's choices and performance evaluation to select the most suited model from available options. In this study, we employed the system on a Parkinson's disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily.Copyright © 2022 The Author(s)

Intelligent predictions of Covid disease based on lung CT images using machine learning strategy.

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms.

Image Processing and IoT-based Fish Diseases Identification and Fish Tank Monitoring System

Every person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world. © 2022 IEEE.

Deep learning hybrid for X-ray lung disease detection

The lung diseases will cause a significant negative effect on the human lungs in a severe manner. A person may suffer from this disease because of bacteria or viruses. The alveoli in the lungs, which are a portion of the lungs that are filled fluids, so the patients with Pneumonia have a low percentage of oxygen in their blood. According to the UNICEF survey, it killed about 880,000 children belonging to the age-group of 0-5 in the year of 2016. Due to the improper detection of the infection in the starting stage, the death rate of the persons increasing enormously. Lung diseases can be detected by radiologists by looking at or examining the chest x-rays very keenly. This process of examining is very costly and requires time. To reduce the time and increase the accuracy of detection, it is needed to prevent the intervention of man from examining the chest x-rays. It is a great idea to use the convolutional neural networks, which includes in the class of deep learning, for the detection of lung diseases. It works on extracting of features from chest x-rays which classifies them to detect lung diseases. Pre-defined architectures of CNNs, which are the state-of-The-Art algorithm and techniques of transfer learning is used in the project. In this study, a Transfer Learning strategy is utilized, in which a previously trained model is utilized to train on images of various lung disorders taken from the dataset, covering safe samples. Some examples of these lung diseases are lung opacity, viral pneumonia, and covid. © 2022 IEEE.

Cross-lingual transfer learning for COVID-19 outbreak alignment

The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. It is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions. © ACL 2020.All right reserved.

Dhanvantari: An intelligent diagnosis tool to classify malignant skin disease and Lung conditions using Deep Learning

The pandemic due to COVID-19 has created a huge gap in the medical field leading to a reduction in the efficacy of this field. To improve this situation, we propose a solution 'Dhanvantari'. A medical app that is powered by Artificial Intelligence performs a task where the diagnosis is done by computer vision observing CT scans, MRIs, and also some skin diseases. Dhanvantari focuses mainly on the combination of CT scans and skin disease classifications. In this paper, a novel approach has been proposed for developing a supervised model for the classification of skin disease and lung ailments (that is to identify a healthy lung with an infected lung due to pneumonia) through analog to digital image processing. This app helps the user in analyzing conditions and if any abnormalities are detected then alerts the user about it. This is a primary service care application developed to reduce the number of false cases hence only alerting the user if a complication is observed. The proposed approach utilizes a camera and computational device or mobile. Two datasets from Kaggle that had 9 classes of malignant skin disease and 2 lung conditions were used to train the model. Design, training, and the testing of the algorithm were performed with the help of colab. Generally, a standard test for malignant skin disease requires sample gathering and conduction of various tests. All these consume a lot of time. The other method is laser or radiation-induced procedures that might be harmful and lead to exposure of unwanted radiation to patients. The proposed 'Dhanvantari' requires the patient/user to use a camera to take a picture of the affected area (in case of skin condition) and it provides the primary diagnosis. This approach aids the doctors in quick decision-making during diagnosis and reduce the time per patient which in house helps them to prioritize patients. © 2022 IEEE.

Deep Network Analysis and Prediction of Ophthalmic Disorders

Everybody, around the globe, is aware that their kids, relatives, and family are suffering from the pandemic COVID-19. S everal people are still facing post-COVID-19 issues. During COVID-19's second wave, mucormycosis, sometimes known as "black fungus, " plagued people, especially those who had previously been infected with the virus. The clinical manifestations of mucormycosis are quite varied, the disease affects the skin, subcutaneous fatty tissue, and visceral organs such as the eyes and brain. This paper surveys the Mucormycosis-affected eye diseases due to post-COVID-19 complications and leverages the Machine learning model to differentiate it from other eye diseases. COVID-19-associated Mucormycosis carries a very high mortality rate and timely detection that can assist people in starting therapy at an early stage of the disease, increasing their chances of recovery. Though it was evaluated for a specific disease (COVID-19-associated mucormycosis) we ended up developing a framework that can detect other eye diseases. Thus, the goal of this research is to distinguish Mucormycosis from other eye diseases such as Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis. This study implies Deep learning techniques with a Convolutional Neural Network based on the TensorFlow and Keras model to detect and make use of computer vision to accurately classify eye diseases. We achieved a precision of 70% in this study by developing a webpage using the trained model for an eye diseases evaluation. © 2022 IEEE

AuNP-Coated Cotton as VOC Sensor for Disease Detection from Breath

The COVID-19 pandemic outbreak, declared in March 2020, has led to several behavioral changes in the general population, such as social distancing and mask usage among others. Furthermore, the sanitary emergency has stressed health system weaknesses in terms of disease prevention, diagnosis, and cure. Thus, smart technologies allowing for early and quick detection of diseases are called for. In this framework, the development of point-of-care devices can provide new solutions for sanitary emergencies management. This work focuses on the development of useful tools for early disease diagnosis based on nanomaterials on cotton substrates, to obtain a low-cost and easy-to-use detector of breath volatiles as disease markers. Specifically, we report encouraging experimental results concerning acetone detection through impedance measurements. Such findings can pave the way to the implementation of VOCs (Volatile Organic Compounds) sensors into smart and user friendly diagnostic devices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

Pre-text Representation Transfer for Deep Learning with Limited and Imbalanced Data: Application to CT-Based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to ‘transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Related Lung Disease Detection and Classification USING a Deep Learning System

COVID-19 is one of the worst illnesses in history is a pandemic. The virus is known as SARS-COVID-2 because researchers have shown that it mostly affects the respiratory system and resembles the SARS variation. In some circumstances, it might cause pneumonia and a collapse of the respiratory system. To diagnose the patients' conditions and ascertain whether lung illness was involved, doctors used X-rays or Computed Tomography (CT) scans. In this study, pulmonary conditions associated with COVID-19 are identified and described using a deep learning method. To diagnose conditions including COV-19, lung cancer, and bacterial pneumonia, the suggested method makes use of CT scan pictures. A 2D picture from a CT scan offers more trustworthy results. The 50 layers of this method are organized into a ResNet-50 convolutional neural network (CNN). Comparing the experimental results to the current methods, a higher yield accuracy is predicted. © 2022 IEEE.

IoT-based disease prediction using machine learning.

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.