A CNN BASED MODEL FOR VENOMOUS AND NON-VENOMOUS SNAKE CLASSIFICATION - DIVA PORTAL
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A CNN Based Model for Venomous and
Non-venomous Snake Classification
Nagifa Ilma Progga1(B) , Noortaz Rezoana1(B) ,
Mohammad Shahadat Hossain1 , Raihan Ul Islam2 , and Karl Andersson2
1
Department of Computer Science and Engineering, University of Chittagong,
Chittagong, Bangladesh
hossain ms@cu.ac.bd
2
Department of Computer Science, Electrical and Space Engineering,
Luleå University of Technology, Skellefteå, Sweden
{raihan.ul.islam,karl.andersson}@ltu.se
Abstract. Snakes are curved, limbless, warm blooded reptiles of the
phylum serpents. Any characteristics, including head form, body shape,
physical appearance, texture of skin and eye structure, might be used
to individually identify nonvenomous and venomous snakes, that are not
usual among non-experts peoples. A standard machine learning method-
ology has also been used to create an automated categorization of species
of snake dependent upon the photograph, in which the characteristics
must be manually adjusted. As a result, a Deep convolutional neural
network has been proposed in this paper to classify snakes into two
categories: venomous and non-venomous. A set of data of 1766 snake
pictures is used to implement seven Neural network with our proposed
model. The amount of photographs even has been increased by utilizing
various image enhancement techniques. Ultimately, the transfer learn-
ing methodology is utilized to boost the identification process accuracy
even more. Five-fold cross-validating for SGD optimizer shows that the
proposed model is capable of classifying the snake images with a high
accuracy of 91.30%. Without Cross validation the model shows 90.50%
accuracy.
Keywords: Snake · CNN · Data augmentation · Deep learning ·
Transfer learning · Cross validation
1 Introduction
Snakes are ectothermic, amniotic reptiles, surrounded in sepals, just like other
squamates. Several snake species have skulls with a slew of joints than their
reptile ancestral, allowing it to swallow predators with their extremely maneu-
verable jaws relatively large unlike there own heads. There are two types of
snakes such as non-venomous (non-poisonous snake) and venomous (poisonous
N. Rezoana—equal contribution.
c Springer Nature Switzerland AG 2021
M. Mahmud et al. (Eds.): AII 2021, CCIS 1435, pp. 216–231, 2021.
https://doi.org/10.1007/978-3-030-82269-9_17
Snake Classification 217
snake). Venomous snakes are members of the suborder Serpents and are able to
develop venom that they use to attack prey, defend themselves and help digest
their prey. Utilizing hollow or grooved fangs, the venom is usually released by
injection, while other venomous snakes lack well-developed fangs. Non-venomous
snakes, except for massive constrictor snakes such as the Green Anaconda or the
Burmese Python, are generally benign to humans. Like venomous snakes, non-
venomous snakes have teeth. Snake envenoming is a major, worldwide common
health issue with the greatest prevalence in Southeast Asia.
Fig. 1. Top 5 countries with the highest rate of snake bite deaths per 100,000 people
A analysis focused on 60 articles has reported that 363 victims with snake
bites, both venomous (88%) and nonvenomous (12%) were diagnosed and treated
if necrosis exists (15.2%) [30]. No infections were detected in patients although
the antibiotics were not used. Thus, based on the analysis, it can be implied
that antibiotics are present in the snake, considering the fact that very little raw
data is given. Inability to identify snake from the visible characteristics is an
important cause of mortality due to snake bite.
For centuries, snake venom, particularly in Chinese culture, have also been
used as medicine tools. Any of the leading drugs for high blood pressure,
cardiovascular disease, and heart attacks used snake venom as a blueprint.
As a consequence, snake venom is often known as a mini-drug repository,
with each medicine being clinically effective. For example, the FDA has
licensed medicines relying on snake venom, such as Captopril R (Enalapril),
IntegrilinR (Eptifibatide), and Aggrastat R (Tirofiban) [24]. Aside from these
approved medicines, various other snake venom materials for numerous thera-
peutic applications are presently in pre-clinical or clinical trials.
Snakebite envenoming is a well-known tropical disease exacerbated by a ven-
omous snake accidentally injecting an extremely modified poison into humans.
The snake’s fangs, that are remodeled teeth attached to a poison glands via
a inlet are used to extract the potion. Nearly 2 million residents in Asia are
218 N. I. Progga et al.
poisoned by snakes every year, although there are approximately 435,000 to
580,000 snake bites annually in Africa that require medication. Women, children
and farmers in vulnerable remote regions in low and middle-income countries
are affected by envenoming. The greatest risk exists in places where health care
services are inadequate and medical facilities are insufficient.
The detection of snake types is a key aspect of the diagnosis process. Classi-
fication and detection of venomous and non-venomous snake has many dynamic
applications such as: It can help the health professional in determining the best
anti-venom to use. Snake venom is often known as a mini-drug repository, so by
classifying the venomous snake from the non venomous snake the venom can be
used to produce more drugs for mankind.
2 Related Work
The Deep Convolution Neural Network (CNN) has carried out in previous
decades a number of groundbreaking findings on image recognition, object iden-
tification, semantic classification etc. [21]. Now it has become prominent in
research for some day because it can handle an immense number of data [4].
CNN outperforms the machine learning approach in terms of efficiency. Classifi-
cation and identification of images was an iconic machine learning issue, which
was overcome through deep learning. In particular, technologies dealing with the
visual information, including the biggest image classification data set (Image
Net), computer vision and NLP analysis were quite impressive and the out-
comes have been obtained is extraordinary. That is why we choose CNN for out
study. The algorithms of deep learning are designed to imitate the role of the
human brain [8]. In biological data [22,23] deep learning has been used. Also in
object detection CNN has shown outstanding performance [11,27,37]. CNN has
already shown remarkable effectiveness in diagnostic image recognition, such as
the detection of lung cancer [14], skin cancer [28], oral cancer [18], brain tumors
[10], and other diseases.
By Using object detection and a machine learning method focused on Mask
Region-based Convolutional Neural Network (Mask R-CNN) has been imple-
mented for snake species classification [7]. To discern reptile species from images,
deep learning, specifically CNN, has been used [5]. Snake sting marks images
were also used to identify poisonous and non-poisonous snakes using the CNN
method [20].
James et al. [16] presented a semiautomatic method in order to differentiate
six distinct organisms by eliminating taxonomical characteristics from the pho-
tos. There were 1,299 photographs in the dataset and 88 photos in the lowest
frequent level. The outer edge taxonomical characteristics are least essential for
organism recognition than the into front and side-view characteristics, according
to various feature detection techniques.
In object detection and image processing, various model architectures have
been evaluated. To discern 5 specific snake varieties Abdurrazaq et al. [2] used
3 distinct Convolutional Neural Network (CNN) frameworks. In this study they
Snake Classification 219
worked with a set of 415 pictures. There were 72 photos required for the less
common snake type. The maximum outcomes were achieved by a medium-sized
classification framework.
The Siamese network was used by Abeysinghe et al. [3] to categorize a com-
paratively limited data set that contains 200 photographs of 84 organisms from
the WHO. The method discussed in their paper focuses on one-shoot learning, as
3 to 16 photos per habitat were provided in the collection of data. The outcomes
collected by the automatic categorization process is lower than the exactness of
human identification accuracy.
Snakebite poisoning is an overlooked environmental diseases which murders
over 100,000 inhabitants and slaughters over 400,000 per year [36]. Snakebite is
a frequent workplace hazard for citizens who make their living in cultivation,
including those in South-East Communities in asia. An automated identifica-
tion of a species of snake depending on its photograph has already been con-
structed, as previously mentioned. As a classifier, a several supervised machine
learning technique has been implemented like Naive Bayes, Decision Tree J48,
k-Nearest Neighbors, or Back-Propagation Neural Network. The requirements
in the features extraction process, on the other hand, are not easy to train in
conventional machine learning techniques and must be manually calibrated. As a
result, throughout this study, important contributions has made in the following
areas:
– A CNN based model has been proposed to classify venomous and non-
venomous snake.
– Various architectures have been compared in terms of performance.
– K-fold cross validation has been applied on our proposed model with three
different optimizer to improves generalization capacity.
– The system can detect both venomous and non-venomous snakes in real time,
allowing non-experts to recognize snake species with greater accuracy than
previously mentioned approaches.
3 Data Pre-processing
At first, we will discuss about the dataset. To initiate, the data set and model
creation plan will be discussed in depth to aid in the planning of the proposed
model. Following that, we’ll go through the proposed model architecture sim-
ulation method in detail, as well as the training methodology for determining
the best parameter modifications. Ultimately, we’ll use modeling approaches to
demonstrate critical flaws in visual indicators in addition to creating a reported
snake more identifiable.
3.1 About Dataset
The set of data comes from kaggle.com and contains about 1766 snake images.
Per photo was assigned to a category and was divided into groups by the respec-
tive class labels such as non-venomous and venomous. Since reformatting is
220 N. I. Progga et al.
among the most important phases of data preprocessing, all images are refor-
matted to 224 × 224 pixels. Figure 3 and Fig. 4 depicts several photographs from
the benchmark dataset. Figures 3 and 4 demonstrate that there are numerous
differences between venomous and non-venomous snakes in terms of physical
appearances such as head structure, eye shape, skin colour, and so on. The men-
tioned features will aid our proposed model in learning the distinctions between
poisonous and non-poisonous snakes. The set of data has been split into train,
validation, and test segments in an appropriate proportion
Fig. 2. Non-venomous snake image
Fig. 3. Venomous snake image
3.2 Data Augmentation
It is well established that even a massive quantity of data in the datasets is
needed to achieve a better result for a CNN model. The data augmentation
techniques is necessary for correctly implementing a CNN architecture. This
methodology prevents data manipulation and maintains the initial reliability.
This technique is often used during the training process to increase the efficiency
of the architecture by fixing overfitting problems. If the dataset is large enough,
several features can be extracted from it and compared to unidentified data.
However, if there is insufficient data, data augmentation may be implemented to
boost the model’s accuracy [9,15,26,33]. Through applying augmentation oper-
ations to training images, such as random rotation, shift, zoom, noise, flips, and
so on, data augmentation will generate multiple pictures [33]. Every parame-
ter has the ability to represent photos in a number of aspects and come up
Snake Classification 221
with particular features during the training phase, increasing the framework’s
effectiveness. Since our dataset is smaller in size, we implemented a variety of
augmentation functions on it. For the augmentation, Image Data Generator was
utilized. Figure 5 represents the primary picture as well as the augmented photos
created from that. We implemented the model with 80% of the overall of the
pictures and used the remaining 20% to validate the system throughout evalua-
tion. The settings for image augmentation used in our experiment can be seen
in Table 1.
Fig. 4. Data augmentation
Table 1. Images augmentation settings
Augmentation setting Range
Rotation 0.2
Zoom 0.1
Contast 0.1
Horizontal flip T rue
4 Methodology
Convolutional neural networks is inspired by neurological mechanisms. A convo-
lutional neural system is made up of many layers, including convolution layers,
pooling layers, and fully connected layers, and it uses a back-propagation algo-
rithm to obtain features to train the model properly. Figure 6 depicts the overall
research’s system flow chart.
4.1 Model Construction
In this study, the framework was implemented using a Convolutional Neural Net-
work (CNN) and data augmentation. First, the architecture uses the dataset to
take the pictures. Then preprocessing begins. Then some augmentation param-
eters are used to enlarge the dataset. Ultimately, the enlarged set of data is
222 N. I. Progga et al.
Fig. 5. System flowchart
used to forecast the class by the CNN architecture. The pictures were stan-
dardised to some extent to be properly categorized. CNN itself performed the
characteristic retrieval of the pictures. We’ll describe our recommended model
architecture in detail in this section. The proposed method includes three basic
components: feature extraction, identification, and classification. In the begin-
ning, we include synchronized layers of convolution, activation, and max-pooling
in our system design. The features are then passed into a flattened layer. The
flattened attributes or features are then transferred into dense layers. In the
dense layer dropout has been used to avoid overfitting. The classification pro-
cess was then completed by the ultimate layer, which stated the softmax layer.
The photos were provided to the model for the training process after the data
augmentation. There is a CBr = iconv block in the system built, accompanied
via a max pooling stratum that is three times in consonance in sequential format.
Maxpooling is an efficient way of downsizing the tightly-scale photos, requiring
maximal rates for each stratum, since most of the features generated are ignored
by utilizing a 3×3 filter size. As discussed during the preceding analysis superim-
posed max pooling frames do not significantly increase over the nonoverlapping
windows, so the max pooling layers used during our experiment was 2 × 2 with
stride 2. It consists of 3 convolutionary layers, with 16 filters of 3 × 3 in the first
convolutional layer,32 filters in the second convolutional layers and the third
layer is 64 layers 3 × 3 in dimension. The scale of the kernel is 2. The produced
features are reused by the attached activation function in the early stages to
construct an unique feature map as output just after the convolution layer. In
equation, convolution over an image q(x, y) is defined using a filter p(x, y).
j
k
p(x, y) ∗ q(x, y) = w(m, n)f (x − m, y − n) (1)
y=−j y=−k
To generate the low-level features all layers of convolution was accompanied
by the ReLU activation function. Following the previous convolution layers, the
ReLU activation function was used in the hidden layer as well. ReLU has a
number of advantages, the most notable of which is its ability to quickly dis-
tribute gradients. As a result, calculating the essential characteristics of CNN
in the provisional mass ,reduces the chances of gradient extinction. The activat-
ing mechanism tends to perform component-by-component operations on this
given input feature map, since the result tends to be the identical dimension as
Snake Classification 223
Fig. 6. Model architecture
the origin. As a result, among the most common activation functions, ReLU is
being used in the all layers. ReLU [12] has been included to demonstrate that
the architecture does not experience linearity, as shown in the corresponding
formula:
R(x) = max(0, x) (2)
The architecture is then exposed in the flattened layers to convert the feature
map. From feature map generated by the previous convolution layers to complete
the categorization task, a single dimensional feature vector has been constructed
by flattened layer. A drop-out layer is linked following the Fully connected or
hidden layer and the size of the fully connected layer is 128. This dropping layer
will dump the weights of the Fully connected layer at random during training
to mitigate unnecessary weight and overfitting. For our CNN model, we choose
0.2 as the dropout range for the drop out layer. The dropout layer’s job is
to discard 20% of the nodes in each FC layer in order to prevent overfitting
[29]. The hyperparameters were fine-tuned by layering again until fault did not
radically alter. In addition, the range of the dropout for the proposed model was
determined by experimenting for several constants. The drop - outs range of 0.2
was considered as the best amount of the dropout layer because it tended to
prevent overfitting rather than others value. In order to maximize the outcome
the parameters are being adapted. Ultimately, since there are two categories
the output layer contains two nodes. The softmax [32] activation function was
used just after the FC layer, as shown in the following equation, to classify
snake images into non-venomous and venomous categories. Figure 8 portrays
the representations of the suggested model’s layers.
224 N. I. Progga et al.
ei
Sof tmax(x) = i (3)
i=0 ei
Fig. 7. The proposed model’s detailed layer representation
4.2 The Implementation Procedure
The coding for the framework was produced and implemented in Google Colab
[6] using the python programming language. Keras [13], Tensorflow [1], NumPy
[35], and Matplotlib [31] were the libraries utilized in the whole study. The back-
end of the framework was chosen as Tensorflow, and keras has been utilized to
offer additional built-in functionality such as activation functions, optimizers,
layers, and so on. Keras API was used to enhance the dataset. NumPy is a
Python library for mathematical evaluation. Confusion matrix, split train and
test files, modelcheckpoint, callback mechanism, as well as other schematic rep-
resentation like confusion matrix, loss against epochs graphs, accuracy against
epochs curves, and many more, are all generated using Sklearn. The matplotlib
library is also needed to create visual representations of the previously mentioned
diagrams, such as the confusion matrix.
5 Experimental Evaluation
The performance of the implemented model to classify photos of snakes specif-
ically grouped into two groups, non-venomous and venomous, will be covered
in this section. In addition, we will compare our proposed model to other tra-
ditional models such as Inception Net, Resnet50, VGG 16, VGG 19, Xception,
MobileNet v2 and Inception Resnet V2. The outcomes of our model’s k fold cross
validation for different optimizers will also be articulated.Snake Classification 225 5.1 Tuning of the Hyper-parameters Since fine-tuned hyper-parameters also have major impact on the CNN archi- tecture’s performance and they are are essential because they strongly impact the model’s attitude. The Adam, Adamax, and SGD optimizers were utilized to trainIing 100 epochs for the proposed model, with a learning rate of 0.0001 with the batch size of 32. K fold cross validation has also been performed for the various optimizers. As the loss function categorical cross entropy was being used, the loss of class probability caused by the softmax function was also determined by this loss function. Finally, calculate each category’s probability of occurrence. ModelCheckpoint has been added as a callback function as well. 5.2 K-Fold Cross Validation To test our model, we attempt 5 fold cross-validation. Due to the general opera- tional overhead, this phase is normally avoided in CNNs. The dataset is divided into three sections using K-folds cross-validation. Fold1 is composed of part 1 as a training set, part 2 as a validation set, and part 3 as testing set, while fold2 is consists of part 2, part 1 and part 3 as a training, validation and testing set respectively. Thus, the fold continues until it reaches 5 folds, with each fold containing unique training, validation, and testing datasets. The K-fold cross validation approach accounts for the utilization of various training and test- ing data, which reduces overfitting and improves generalization capacity. As a consequence, we may generalize our outcomes over the dataset. 5.3 Result Figure 8 depicts the accuracy of the proposed model as compared to other stan- dard Convolution neural networks like Inception Net, VGG 19, Resnet50, Xcep- tion Net, MobileNet v2, Inception Resnet V2 and VGG 16. Despite providing a dataset with a limited number of photographs, the designed model produces a high categorization accuracy cpmpared to other CNN models. As shown in the Fig. 9 Inception Net, VGG 19, Resnet50, Xception Net, MobileNet v2, Inception Resnet V2 and VGG 16 has 82.38%, 43.75%, 81.81%, 80.94%, 82.35%, 89.62% and 62.50% accuracy respectively. Our model outperformed then these models with 90.50% accuracy. The best result corresponds to the model we introduced, based on the current Inception Net, VGG 19, Resnet50, Xception Net, MobileNet v2, Inception Resnet V2, and VGG 16 models. As a result, it can be concluded that the developed framework outperforms the other ones. The performance of all evaluation models, as seen in Fig. 8, indicate that the method is superior to others. Figure 9 compares the proposed model’s accuracy and loss curve to those of other traditional CNN models. From the shape of the curves in this mentioned figure, we can see that other convolutional models have a propensity to overfit or underfit, while our proposed model seems to have a good fit. Our proposed model’s high accuracy and low loss have already been considered the best case scenario for any CNN model. Figure 10 shows the detection performance of the
226 N. I. Progga et al.
Fig. 8. Accuracy of proposed model & other traditional models
proposed model and shows the detection result of the test dataset’s in two classes:
venomous and nonvenomous. In comparison to the previously listed other CNN
models, 40 images were arbitrarily checked and 9 of those being seen in Fig. 6.
The identification performance for the developed method is really exceedingly
decent. The confusion matrices of the Inception Net, VGG 19, Resnet50, Xcep-
tion Net, MobileNet v2, Inception Resnet V2, and VGG 16 models, as well as
the suggested system, are shown in Fig. 11. For our adapted CNN model, the
diagonal magnitude of the confusion matrices in both classes is greater than the
other models. That is, the proposed model will precisely distinguish the same
number of test samples from our test dataset as the current Neural network
model. As a result, our model successfully outperformed the other conventional
Neural network model in this area as well. Finally, Fig. 12 demonstrates the
K-fold cross validation result of the proposed model utilizing three optimizers:
Adamax, Adam, and SGD. By using adamax optimizer for K fold cross vali-
dation the validation accuracy and the testing accuracy for fold-1 is 83.58% &
86.87%, for fold-2 it is 81.83% & 82.52%, for fold-3 it is 77.44% & 80.07%, for
fold-4 it is 84.24% & 84.96& and for fold-5 it is 86.87% & 83.20% respectively.
For adam optimizer the validation accuracy and the testing accuracy for fold-1
is 86.56% & 89.22%, for fold-2 it is 87.57% & 88.04%, for fold-3 it is 83.35% &
84.42%, for fold-4 it is 87.78% & 90.48% and for fold-5 it is 84.36% & 88.43%
respectively. For SGD optimizer the validation accuracy and the testing accu-
racy for fold-1 is 84.85% & 85.78%, for fold-2 it is 88.30% & 89.67%, for fold-3 it
is 84.78% & 85.61%, for fold-4 it is 83.77% & 88.04% , and for fold-5 it is 87.73%
& 91.30 respectively. The SGD optimizer in fold-5 produces better outcomes for
the implemented model, as well as the other results are also quite satisfactory.
Also it demonstrates that the accuracy of the classification model is unaffected
by the training data.Snake Classification 227
(a) InceptionNet (b) VGG19 (c) Resnet50 (d) Xception
(e) Mobilenet v2 (f) Inception- (g) VGG16 (h) Proposed
Resnet-v2 Model
Fig. 9. Accuracy and loss curve
Fig. 10. Result of detection produced by the proposed model228 N. I. Progga et al.
(a) InceptionNet (b) VGG19 (c) Resnet50 (d) Xception
(e) Mobilenet v2 (f) Inception (g) VGG16 (h) Proposed
Resnet v2 Model
Fig. 11. Confusion matrix of the models
(a) adamax Optimizer (b) Adam Optimizer (c) SGD Optimizer
Fig. 12. K-Fold cross validation for proposed model using different optimizer
6 Epilogue and Future Work
A new convolutional neural network-based architecture for detecting and classi-
fying venomous and non-venomous snakes was suggested during this whole study.
The framework’s ability to acquire snake features using neural network blocks is
clearly demonstrated. In comparison to the various possibly the best Convolu-
tional neural network frameworks Inception Net, VGG 19, Resnet50, Xception
Net, MobileNet v2, Inception Resnet V2, and VGG 16, the architecture has
remarkable categorization accuracy.
This study looks at how to develop and create a venomous and nonvenomous
snake classification model that could help mankind. Snake venomous can be
used as medicinal tools by distinguishing between venomous and non-venomous
snakes. Snakebite disease may be minimized by identifying the species of snake
and administering appropriate treatment.Snake Classification 229
The suggested solution greatly outshines state-of-the-art frameworks, with a
dramatic increase in accuracy of 90.50%, according to the experimental research
review. In addition, the model performs admirably in terms of K fold cross val-
idation outcomes. It would have been more useful in upcoming analysis if we
perform a grid hunt on the hyper-parameters and find the most suitable num-
ber of parameter values for K fold cross validation. Finally, we believe that the
current findings would hopefully resolve unique stimulation in recognizing addi-
tional snake images and categorizing them in Artificial intelligence based envi-
ronments, particularly in medical application. To create our suggested structure
more reliable and authenticated, more analysis should be done to evaluate and
gather a diverse data set with significant quantities of information about snake
photographs. Furthermore, many additional Neural networks, such as DenseNet,
EfficientNet, and others, can be used to enhance information horizons, and the
model’s output can be compared to that of other mainstream machine learning
techniques. We will try to include more pictures of snake in future captured
in different environment and lightening to analyze the model performance on
those images. Also an integration of data-driven (CNN) and knowledge-driven
(BRBES) approach can be proposed to analyze risk assessment of a snake bite
in the human body [17,19,25,34].
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