Deep Convolutional Network Based Saliency Prediction for Retrieval of Natural Images

Nandagopalan, S.; Kumar, Pandralli K.

doi:10.1007/978-3-030-00979-3_51

Cited by 4 publications

(1 citation statement)

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“…The key difference from the existing systems with our system is that they did not use a custom CNN model for classifying the mosquitoes whereas our system has done that. We know that CNN models perform better than other approaches in classifying images as well as retrieval of images [28]. Therefore, custom CNN model has been used as well as other pre-trained CNN models like VGG-16 to compare their performances.…”

Section: Related Workmentioning

confidence: 99%

Mosquito Classification Using Convolutional Neural Network with Data Augmentation

Akter

Hossain

Ahmed

et al. 2021

Advances in Intelligent Systems and Computing

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Mosquitoes are responsible for the most number of deaths every year throughout the world. Bangladesh is also a big sufferer of this problem. Dengue, malaria, chikungunya, zika, yellow fever etc are caused by dangerous mosquito bites. The main three types of mosquitoes which are found in Bangladesh are aedes, anopheles and culex. Their identification is crucial to take the necessary steps to kill them in an area. Hence, a convolutional neural network (CNN) model is developed so that the mosquitoes could be classified from their images. We prepared a local dataset consisting of 442 images, collected from various sources. An accuracy of 70% has been achieved by running the proposed CNN model on the collected dataset. However, after augmentation of this dataset which becomes 3,600 images, the accuracy increases to 93%. We also showed the comparison of some methods with the CNN method which are VGG-16, Random Forest, XGboost and SVM. Our proposed CNN method outperforms these methods in terms of the classification accuracy of the mosquitoes. Thus, this research forms an example of humanitarian technology, where data science can be used to support mosquito classification, enabling the treatment of various mosquito borne diseases.

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Section: Related Workmentioning

confidence: 99%