Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Use of Artificial Intelligence on the Control of Vector-Borne Diseases

Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review

An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures. Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples. Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase. Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates. Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases

Delimiting Cryptic Morphological Variation among Human Malaria Vector Species using Convolutional Neural Networks

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiaecomplex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance

Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks

In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources. In this paper, we identify a set of (non-exhaustive) requirements that smartphone apps must meet to become an effective tooling in the fight against Ae. aegypti, and advance the state-of-the-art with (i) a residual convolutional neural network for classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a methodology for reducing the influence of background noise in the classification process, and (iii) a dataset for benchmarking solutions for detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the analysis of accuracy and recall, we provide evidence that convolutional neural networks have potential as a cornerstone for tracking mosquito apps for smartphones

Mosquito chronological age determination using mid-infrared spectroscopy and chemometrics

Determining a mosquito population’s species composition and age is crucial for estimating the risk of pathogen transmission. At present, age-grading methods are chiefly physiologic and classify the mosquitoes in terms of parity (e.g., nulliparous or parous). Less commonly used chronologic methods (e.g., qPCR or near infrared spectroscopy [NIR]) have limited temporal resolution (NIR) or require consumable reagents and technological expertise with molecular methods. The current lack of robust methods to rapidly evaluate a population’s chronologic age limits our ability to assess pathogen transmission risk in the context of vectorial capacity estimations (i.e., daily survivability). Our current research seeks to develop methods of mosquito age determination utilizing mid-infrared spectroscopy and advanced numerical analysis(chemometrics). Infrared (IR) spectroscopy is a type of vibrational spectroscopy that is both sensitive and information rich. Subtle changes in IR spectra correlate with changes in the biochemistry of mosquitoes as they age. It has been shown that mosquito species can be identified using mid infrared spectroscopy and chemometrics. Using mid-infrared spectroscopy and chemometrics, the chronologic age of Aedes triseriatus mosquitoes were predicted using PLSR and ANN models. Aedes triseriatus were successfully reared into groups of different ages with low uncertainty in the age. Aedes triseriatus spectra were used to create a training dataset and fit models for prediction using PLSR and ANN. PLSR and ANN models were used to predict the age of samples using a validation dataset with SEPsv of 4.3 and 3.3 days respectively. Mean spectra for each age group were used to try and discern a specific chemical underpinning for the performance of these models and to explain why mosquito age could be predicted using PLSR and ANN models. Peaks between 1200 – 1000 cm-1 typically associated with chitin were investigated and the second derivative of mean absorbance by age at 1032 cm-1increased linearly with age