Snake classification from images

James, Alex Pappachen

doi:10.7287/peerj.preprints.2867

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…James 9 proposed a method that included manually cropping of 31 taxonomic features from snakes’ head and body images. Snake features are subsequently classified using the proposed method based on k NN algorithm.…”

Section: Discussionmentioning

confidence: 99%

A comparative study on image-based snake identification using machine learning

Rajabizadeh

Rezghi

2021

Sci Rep

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Automated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. In this study, for the first time, it’s been attempted to compare the accuracy of a series of state-of-the-art machine learning methods, ranging from the holistic to neural network algorithms. The study is performed on six snake species in Lar National Park, Tehran Province, Iran. In this research, the holistic methods [k-nearest neighbors (kNN), support vector machine (SVM) and logistic regression (LR)] are used in combination with a dimension reduction approach [principle component analysis (PCA) and linear discriminant analysis (LDA)] as the feature extractor. In holistic methods (kNN, SVM, LR), the classifier in combination with PCA does not yield an accuracy of more than 50%, But the use of LDA to extract the important features significantly improves the performance of the classifier. A combination of LDA and SVM (kernel = 'rbf') is achieved to a test accuracy of 84%. Compared to holistic methods, convolutional neural networks show similar to better performance, and accuracy reaches 93.16% using MobileNetV2. Visualizing intermediate activation layers in VGG model reveals that just in deep activation layers, the color pattern and the shape of the snake contribute to the discrimination of snake species. This study presents MobileNetV2 as a powerful deep convolutional neural network algorithm for snake image classification that could be used even on mobile devices. This finding pave the road for generating mobile applications for snake image identification.

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Section: Discussionmentioning

confidence: 99%

A comparative study on image-based snake identification using machine learning

Rajabizadeh

Rezghi

2021

Sci Rep

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Life-Saving APP: Snake Classification ‘Venomous and Non-venomous’ Using fast.ai Based on Indian Species

Lakshmi

Panda²,

Amrita

et al. 2021

Artificial Intelligence Systems and the Internet of Things in the Digital Era

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Snake species classification using deep learning techniques

Ahmed,

Gad,

Aboutabl

2023

Multimed Tools Appl

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Incorrect snake identification from the observable visual traits is a major reason of death resulting from snake bites. The classification of snake species has a significant role in determining the appropriate treatment without any delay, the delay may cause dangerous complications or lead to the death of the victim. The difficulty of classifying snakes by human lies in the variations of snake pattern based on geographic variation and age, the intraclass variance is high for specific classes and the interclass variance is low among others, and there may be two remarkably similar types in shape, with one being toxic and the other not. The limitation of the experts’ number in the herpetology and their geographical distribution leads us to the importance of using deep learning in the snake species classification. A model to classify snake species accately is proposed in this study. It is divided into two main processes, detecting the salient object by applying Salient Object Detection (SOD) model based on VGG16 architecture is the first process, the presence of snakes in places with a complex background led to the necessity of separating the salient object, then the classification model is applied with use of image augmentations parameters which improved the results. Four CNN models were used in the classification process including VGG16, ResNet50, MobileNetV2, and DenseNet121. Different experiments on 5,10,16,20, 22, and 45 number of classes and different models were conducted, and the model achieved unprecedented results. The results indicated that the VGG16, DenseNet121, and MobileNetV2 have achieved superior results in the same order from highest to lowest accuracy. The best accuracy is achieved using VGG16 architecture with accuracy 97.09% when using 45 number of classes.

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Snake classification from images

Cited by 3 publications

References 25 publications

A comparative study on image-based snake identification using machine learning

A comparative study on image-based snake identification using machine learning

Life-Saving APP: Snake Classification ‘Venomous and Non-venomous’ Using fast.ai Based on Indian Species

Snake species classification using deep learning techniques

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