Mobile Diagnosis System Research Articles

A new mobile diagnosis system for estimation of crop disease severity using deep transfer learning

Crop diseases pose as a major threat to global food security. Minimizing disease-induced damage during crop growth and optimizing crop yields are vital for agricultural sustainability. Therefore, advanced disease detection and prevention of such diseases are crucial and the detection must be prompt and efficient as it is essential for the implementation of appropriate control measures. In this work, a parallel deep learning framework based on deep feature fusion is developed to precisely identify the severity of crop diseases. The framework utilizes ResNet50 and Xception as separate branches for feature extraction. Convolutional layer weights are initialized through transfer learning techniques employing models pre-trained on the ImageNet dataset. A fine-tuning strategy is employed for the optimization of convolutional layers and the design of the top layer. This framework achieves an accuracy of 88.58% on the AI Challenger 2018 dataset, marking an enhancement of 2.8% and 13% over other influential deep learning models, and it also outperforms some recent works. Likewise, the framework exhibits a recognition accuracy of 99.53% on the PlantVillage dataset. Moreover, an Android-based application is developed to diagnose the severity of crop diseases in real-time. The diagnostic system swiftly procures results and provides control recommendations through the capture and upload of images to the platform. The advanced severity detection system reduces the expertise required from users, facilitating precise prevention and control measures whilst focusing on accessibility. This work aims to provide innovative approaches and solutions for disease detection in the agricultural field, utilizing artificial intelligence to enhance agricultural informatization and creating more sustainable farming methods.

Offline mobile diagnosis system for citrus pests and diseases using deep compression neural network

This study presents an offline mobile diagnosis system for citrus pests and diseases by compression convolutional neural network. Recently, with the growth of labelled data, the deep neural network incites the revolutionary change with a quantum leap in various fields. Benefiting from the backpropagation method, the proper network structure can automatically extract high-level representations and find corresponding labels. The authors made use of the advantages of the deep neural network to design an android application, which can be installed in any stand-alone devices to instantaneously identify the citrus pests and diseases. The proposed diagnosis system has three characteristics: low cost, low latency and high accuracy. These characteristics contribute to make the professional offline prediction for avoiding further economic loss caused by disease spreading. In order to validate the proposed system, the authors conducted thorough evaluations on two data sets, `citrus pests and diseases', CIFAR, which show the superiority of the proposed approach in terms of the accuracy and the number of model parameters.

Ensemble sınıflamdırıcı tabamlı mobil tiroit tanısı

The thyroid gland plays a major role in many metabolic activities of the human body. Thyroid disease, which is quite common in humans, affects people's quality of life significantly. Early diagnosis is very important for taking precautions. The mobile diagnostic system can be the solution for early diagnosis especially in rural areas or without going to health institution. This study has been proposed to enable people with mobile devices to obtain quick information about the disease or to seek medical assistance in any matter without going to the hospital. Functional thyroid diagnosis system is designed using mobile device, Android based software application, Database (SQL) and Server (MATLAB based decision algorithms). With the system, functional thyroid disease can be diagnosed using an android based mobile device. Different classification algorithms were searched for the most accurate diagnosis and Ensemble method which has a high success rate for thyroid disease was used in the system. Ensemble classification technique reached a success rate of 99.06% and 99.08% for the first and second data group, respectively. These success rates were calculated by using gold standard test and results were compared with the literature. Obtained test results showed that, the proposed mobile diagnosis system could be used for the diagnosis of the functional thyroid. At the same time, this system can be developed for different diseases.

Heartbeat classification by using a convolutional neural network trained with Walsh functions

From recent studies, it is observed that convolutional neural networks are proved to be extremely successful in classification problems. Accurate and fast classification of electrocardiogram (ECG) beats is a crucial step in the implementation of real-time arrhythmia diagnosis systems. In this study, convolutional neural networks are employed to classify eleven different ECG beat types in the MIT-BIH arrhythmia database. We aimed to implement a computer-aided mobile diagnosis system equipped with artificial intelligence that detects the classes of heartbeats by visual inspection of the ECG records in the manner as the cardiologists do. Since doctors make their decisions basing heavily on the 2D visual appearances of the ECG signals without doing numerical calculations on 1D time samples, 2D images of 1D ECG records were given to the classifier as the input data. It would not be surprising that the structure of the network classifying 2D ECG data has to be larger than the one used to classify 1D ECG signals. The small size of a neural network is an important property for real-time use of the system. In this study, smaller network structures that provide high performances using the Walsh functions (WF), and drawbacks of converting 1D signals to 2D images have been investigated. The network structures using the WF during the training stage have been applied to different databases and successful results have been obtained. Classification results and sizes of the network structures are compared for the ECG beats in one dimension and in the form of 2D visuals. The training of ECG signals in the form of 2D visuals takes much longer time than that of the 1D signals. However, it is observed that training and testing times of both networks were quite fast. Moreover, average success rates of 99% were achieved for all beat types by using small-size networks.

A Smart Mobile Diagnosis System for Citrus Diseases Based on Densely Connected Convolutional Networks

Citrus is one of the most widely cultivated fruit in the world. However, citrus diseases are becoming more and more serious, which has caused substantial economic losses to citrus growers. With the rapid developments of mobile device, mobile services computing play an increasingly important role in our daily lives. How to develop an intelligent diagnosis system for citrus diseases based on mobile services computing and bridge the gap between citrus growers and plant diagnostic experts is worth studying. In this paper, we build an image dataset of six kinds of citrus diseases with the help of experts and realize an intelligent diagnosis system for citrus diseases by constructing the simplified densely connected convolutional networks (DenseNet). The system is realized using the WeChat applet in the mobile device, with which users can upload images and receive diagnostic results and comments. The experimental results show that the recognition accuracy of citrus diseases exceeds 88% and the predict time consumption has also been reduced by simplifying the structure of the DenseNet.

Mobile Diagnosis System with Emergency Telecare in Thailand (MOD-SET)

Abstract This paper presents a Mobile Diagnosis System with Emergency Telecare functionality applied in Community Health Centers and hospitals in Thailand. The system results from an integration of two previously released applications: a tele-diagnosis system (TEDIST) and a smartphone based emergency report system (SPEARS). The functionality of these separate systems have been integrated and enhanced with a speech-to-text function, which users (or potential patients) apply to communicate with the system by speaking into their phones. A rule-based expert system is controlled by medical professionals regarding the automated diagnosis results. The emergency response component can communicate with local emergency services from hospitals and other organizations in order to find the nearest facility for help and support in case of emergency. Tests of the user interface and system tests have been carried out and have shown encouraging results.

An intelligent mobile based decision support system for retinal disease diagnosis

Diabetes and Cataract are the key causes of retinal blindness for millions of people. Current detection of diabetes and Cataract from retinal images using Fundus camera is expensive and inconvenient since such detection is not portable and requires specialists to perform an operation. This paper presents an innovative development of a low cost Smartphone based intelligent system integrated with microscopic lens that allows patients in remote and isolated areas for regular eye examinations and disease diagnosis. This mobile diagnosis system uses an artificial Neural Network algorithm to analyze the retinal images captured by the microscopic lens to identify retinal disease conditions. The algorithm is first of all trained with infected and normal retinal images using a personal computer and then further developed into a mobile-based diagnosis application for Android environments. The application is optimized by using the rooted method in order to increase battery lifetime and processing capacity. A duty cycle method is also proposed to greatly improve the energy efficiency of this retinal scan and diagnosis system in Smartphone environments. The proposed mobile-based system is tested and verified using two well-known medical ophthalmology databases to demonstrate its merits and capabilities. The evaluation results indicate that the system shows competitive retinal disease detection accuracy rates (>87%). It also offers early detection of retinal diseases and shows great potential to be further developed to identify skin cancer.

Diagnostic testing of valves fitted with electric actuators / Diagnoseprüfungen an Armaturen mit Stellantrieben

A mobile diagnosis system for valves fitted with AC actuators was developed. This system records electrical and mechanical data during valve actuation. Evaluation of this data makes it possible to assess reliably the functional status of the valve/actuator unit and to establish incipient or existing deficiencies. Appropriate maintenance measures can then be initiated