Aedes Aegypti Larvae Detection System Based On Convolution Neural Network Via Transfer Learning

Aedes aegypti mosquitoes are small slender fly species spreading the arbovirus from flavivirus vector through the feeding of the mammals’ blood. The early detection of this species is very important. Once this species turns into adult mosquitoes, the population control becomes more complicated. The situation even worse when difficult access places like a water storage tank became one of the favourite breeding places for the Aedes aegypti mosquitoes. Therefore, a technological method is required to assist the operator in the field during the routine inspection of the Aedes aegypti larvae, especially at difficult access places as stated in the report of the World Health Organization (WHO). This research proposed a development of the Aedes aegypti larvae detection system based on the convolutional neural network via the transfer learning method. In this study, a database is created since there is no Aedes aegypti database available online. The database is developed by collecting the Aedes aegypti larvae images in in the same environment of water storage tank. 507 images are set for training dataset, 10 images for validation dataset and 30 images for test dataset. Two different convolutional architectures have been trained in this study, which are Faster-Region Convolutional Neural Network (Faster-RCNN) and Single Shot Multibox Detector (SSD) that applying same region proposal techniques and base network of Inception-v2. Besides, the pre-trained model of the Common Object in the Context dataset has been applied in this training, where the hyper-parameter fine-tune configuration has been implemented in this study. The performance of the generated inference graphs is analysed based on three main aspects, which are the performance during training, validation and test. In order to estimate the generalization gap in the training phase, the cross-entropy loss of the training and the validation for both architectures are obtained so that the optimum capacity can be retrieved from the learning. Meanwhile, in the validation phase, the tracking-based metrics and the perimeter intrusion detection metrics are conducted for several specific learning steps in the validation dataset. The precision-recall curve (PR Curve) also has been implemented in the validation phase, where the curve at the right top angle is proposed as the best model in this study. In the test phase, the test dataset is tested with standard detection metrics. From the results obtained in the training, validation and test analyses, it is observed that the best architecture for the detection of the Aedes aegypti larvae is the Faster-RCNN. The results also indicated that the accuracy of the test results for the Faster-RCNN is 0.9213, while the SSD is 0.6966. Therefore, it can be concluded that the Faster-RCNN is the best model in the detection of the Aedes aegypti larvae. The impact of this study is the proposal of a new method with respect to vision technology, specifically for the Aedes Aegypti larvae prevention and outbreak as highlighted by WHO and sustainable development programme by United Nation

Detection Of Aedes Aegypti Larvae Using Single Shot Multibox Detector With Transfer Learning

The flavivirus epidemiology has reached an alarming rate which haunts the world population including Malaysia. In fact, World Health Organization has proposed and practised many methods of vector control through environmental management, chemical and biological orientations but still cannot fully overcome the problem. This paper proposed a detection of Aedes Aegypti larvae in water storage tank using Single Shot Multibox Detector with transfer learning. The objective of the study was to acquire the training and the performance metrics of the detection. The detection was done using SSD with Inception_V2 through transfer learning. The experimental results revealed that the probability detection scored more than 80% accuracies and there was no false alarm. These results demonstrate the effectiveness of the model approach

Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae

The flavivirus epidemiology has reached an alarming rate which haunts the world population including Malaysia. World Health Organization has proposed and practised various methods of vector control through environmental management, chemical and biological orientations. However, from the listed control vectors, the most crucial part to be heeded are non-accessible places like water storage and artificial container. The objective of the study was to acquire and compare various accuracies and cross-entropy errors of the training sets within different learning rates in water storage tank environment which was essential for detection. This experiment performed transfer learning where Inception-V3 was implemented. About 534 images were trained to classify between Aedes Aegypti larvae and float valve within 3 different learning rates. For training accuracy and validation accuracy, learning rates were 0.1; 99.98%, 99.90% and 0.01; 99.91%, 99.77% and 0.001; 99.10%, 99.93%. Cross-entropy errors for training and validation for 0.1 were 0.0021, 0.0184 whereas for 0.01 were 0.0091, 0.0121 and 0.001; 0.0513, 0.0330. Various accuracies and cross-entropy errors of the training sets within the different learning rates were successfully acquired and compared

A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing

A Review On Methods Of Identifying And Counting Aedes Aegypti Larvae Using Image Segmentation Technique

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Combining of transfer learning with faster-RCNN for Aedes Aegyti larvae detection

The dengue epidemiology episode has become one of the global phenomena especially the rain forest countries including Malaysia. Environmental management, the used of chemical and biological environment are control strategies that has been proposed and practiced by World Health Organization. However, based on statistic al of dengue cases, there is still no concrete solution in curbing this problem especially at non-accessible places. This paper proposed a study on detection Aedes Aegypti larvae in water storage tank by combining transfer learning with Faster-RCNN. The purpose of the study is to acquire train and validation losses along with detection performance metrics. The experimental results disclose that the probability detection has scored 97.01% while false alarm has scored 5.97%. Those significant value has depicted that the trained model has high detection accuracie