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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5234_Библиотеки_им_академика_М_И_Перельмана
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164 Computational Intelligence Algorithms
x
)
J a =
−
1
˜
a ˇ
ˆ
x
J
()
()
a
v
q
wh
˜
g
)
a ˙
˝
x
J
v
q
FIGURE 12.1 Architecture of adaptive convolution neural network–based brain tumor
detection framework.
be used to determine this weight. The weights depend on the physical discrepancies and the average length between pixels. The ltering of noise is obtained from
Equation (12.1).
f
()
()
v
q
Ja
()gk(| Ja
x
Ja ha
()
()|(l − a
x
(12 .1)
Here,
that needs to be denoised, and
ltered.
f
represent the noise-removed image, J denotes the actual image
represents the matching ratio of the pixel to be
represents the average length between pixels
and denotes the weight
of each pixel. x represents the Gaussian range to preserve edges. l denotes the
intensity values of the pixel. The standardization of the equation is shown in
Equation (12.2):
=
x
− ()
(( Jaha
k Jax
l( x
− a
(12.2)
denotes the actual image that needs to be denoised; a represents the matching
ratio of the pixel to be ltered.
represents the average length between pixels
and denotes the weight of each pixel. x represents the Gaussian range to preserve
edges. l denotes the intensity values of the pixel. The stages in preprocessing are
shown in Figure 12.2. The bilateral lter obtains the normalization of the image.
The ltering process is concentrated on the intensities of surrounding pixels.
The Gaussian range is xed around the surrounding pixel values, and the ltered
image is obtained.
12.3.2 SEGMENTATION
There are a lot of approaches that work well for segmenting MRI pictures, but picking just one or two is not enough for every image. Because of its adaptability and
165 Adaptive Convolution Neural Network-based Brain Tumor Detection
ab,
)
)
(
()
FIGURE 12.2 The stages in preprocessing.
ability to handle the high complexity of MRI images, the suggested framework uses
the adaptive thresholding method, as shown in Equation (12.3):
()
The selection of intensity values is based on the threshold value A; the MRI
image with the pixel values is represented as
histogram and a variation of intensity values can be used to choose a threshold value
(A). Over the whole picture
output identied the precise locations of the tumor areas. The range of threshold
values 0,1 obtains the location of tumor cells. The accurate location of tumor cells is
shown in Figure 12.3.
if ab A
0,(,)
, the threshold value shouldn’t change. This step’s
if ab A
1,,
b
=
,
>
(12.3)
≥
. Depending on condition 1 0, the
FIGURE 12.3 The precise location of tumor cells.
166 Computational Intelligence Algorithms
A
x
x
= [,1 R ,..]
A R 2 … R
m
S
n
L
S
N =1
S
n
12.3.3 FEATURE EXTRACTION
Dimensional reduction analysis is used to extract features from the segmented image.
Dimensional reduction analysis is a powerful mathematical tool for decorrelating
massive datasets, including linked variables. It is an x-dimensional linear transformation applied to an array (x) of picture rows and columns. The feature extraction
is obtained by regulating the image with the number of rows and columns. Note
from Equation (12.4):
(12.4)
The rows of the pixel matrix are represented as R, and the column representation is denoted as m. The covariance of the pixel matrix
Equation (12.5):
1
= A (12.5)
n
L
˜
x
L represents the number of rows and columns, N represents the features in the
image, x represents the covariance of the pixel matrix. The features from the segmented image are obtained from the covariance of the pixel matrix.
stage of the process is classifying tumor and nontumor cells.
is obtained from
. The next
12.3.4 CLASSIFICATION BY ADAPTIVE CONVOLUTION NEURAL NETWORK
ACNN includes mathematical operation and three main components in a neural
network: input layer, hidden layer, and output layer. The hidden layer performs
traditional techniques like pattern recognition. A neural network is parallel computing and performs computational tasks, including pattern recognition, classication, optimization, approximation, and data clustering. Any deep neural
network model needs a lot of data to train and test the model and a lot of computing resources. When comparing regular neural networks, ACNN is the best for
understanding the classication of an image. ACNN extracts the features and
identies patterns of the image dataset. The process inside the ACNN model is
executed by the input image (32 × 32 × 3) with the conv2D with the number of
lters and rectied Linear Unit Relu(). To generate a down-sampled (pooled) features chart, the max pooling procedure determines the highest result for featuremapped areas. Dropout is used to avoid overtting the pixel. The complete stages
are shown in Figure 12.4.
ACNNs use max pooling as an operation. The initial volume’s geometrical
dimensions (width and height) are reduced using a down-sampling method. This
lowers the processing effort and the amount of factors, which helps minimize
overtting. The max pooling method splits the input into rectangle subregions
that do not intersect and then returns the most signicant amount for each
subregion.
167 Adaptive Convolution Neural Network-based Brain Tumor Detection
FIGURE 12.4 Stages of adaptive convolution neural network.
The duration and lter capacity are the primary variables of max pooling. The
stride controls the amount the lter travels along the input, while the lter size
denes the measurements of the zone from which the highest number is extracted.
The maximum pooling operation (2 × 2) is shown in Figure 12.5.
To enhance the attention mechanism, max pooling can streamline: that is, it
simplies the system’s concentration on key characteristics by simplifying inputs
for the attention levels. The attention system can improve the extraction of attributes by giving larger weights to signicant elements via max pooling. The function of ACNN is shown in Figure 12.6. ACNN achieves the classication of tumor
and nontumor cells.
FIGURE 12.5 The maximum pooling operation (2 × 2).
168 Computational Intelligence Algorithms
FIGURE 12.6 Function ow of our proposed CNN model.
12.4 RESULT AND DISCUSSION
The experimental evaluation was done using Google Colab and Python. The dataset
collection is from, including benchtop magnetic resonance imaging (BT MRI) and
non−BT MRI images, and the dataset is mounted onto Google Drive. Our BT MRI
image dataset contains two folders, YES and NO, containing 253 MRI images of the
brain. The YES folder contains 155 tumorous MRI images. The NO folder contains
98 nontumorous MRI images. The data augmentation technique operates on rotation, scaling, translation, and cropping and is applied to the MRI image (Brain) to
increase the high quality of (MRI) brain images. The normalization technique is
used for image standardization, and the pixel scaling factor is 0-1.
TABLE 12.1
The Accuracy between Sigmoid and Softmax Function
169 Adaptive Convolution Neural Network-based Brain Tumor Detection
Optimization
RMSprop
RMSprop
Activation Function
Sigmoid
Softmax
Splitting Ratio
9:1
9:1
Accuracy
95.59%
99.82%
Data splitting is in the ratio of 9:1, i.e., 90% for the training set and 10% for
the testing set. The model is trained for ten epochs. Table 12.1 shows the accuracy
between the sigmoid () and Softmax () activation functions using the root mean
square (RMS) prop optimizer. The plots shown in Figure 12.7−12.10 show the loss
and accuracy of the training and validation (testing) model. In our ACNN model,
automatic brain tumor (BT) detection is performed very efciently, achieving an
accuracy of 99.82%. Table 12.1 shows the accuracy between the sigmoid () and
Softmax() activation functions used in the fully connected layer, and model optimization was done using RMSprop. The complete process for detecting and classifying
tumor and nontumor cells is achieved by utilizing accuracy and activation functions.
The activation function with the convolutional lters and kernel helps achieve the
highest classication accuracy with the splitting ratio.
Training is about running the dataset through the process for a predetermined
number of epochs. The accuracy metrics are monitored to evaluate the model’s generalizability to new data. The Training Accuracy metric shows how accurately the
algorithm matches the data used for training. When a model achieves a high training accuracy, it has successfully mastered the trends found in the training data. The
Validation Accuracy metric shows how well the model can apply its ndings to other
FIGURE 12.7 Accuracy by using the sigmoid activation function.
170 Computational Intelligence Algorithms
FIGURE 12.8 The loss by using the sigmoid activation function.
datasets. Overtting can occur if this parameter is not closely monitored. Figu re 12.7
shows the accuracy using the sigmoid activation function.
One way to evaluate a system’s efcacy on training data is by looking at its
training loss. The sigmoid activation mechanism produces a probabilistic result;
this result can then be evaluated to the actual values utilizing a loss function,
like binary cross-entropy. Figure 12.8 shows the loss using the sigmoid activation
function.
FIGURE 12.9 Accuracy by using the softmax activation function.
171 Adaptive Convolution Neural Network-based Brain Tumor Detection
FIGURE 12.10 Training loss by using the softmax activation function.
The softmax activation function nds extensive application in the network’s output layer by training neural networks for multiclass sorting. Transferring a class
name according to the highest likelihood is easier by converting the raw prediction
values into changes that add up to one. Figure 12.9 shows accuracy by using the
softmax activation function.
For jobs involving several classes to be classied, using the softmax activation
function in neural networks is crucial. This function gives a probability distribution over the classications. In conjunction with softmax activation, the categorical
cross-entropy loss function allows the network to learn by reducing the discrepancy
between the actual and expected label patterns. Figure.12.10. shows the loss by the
softmax activation function.
Table 12.2 shows the accuracy performance comparison with the existing
method: CNN [21], RViT [23], ExpDHO-based ShCNN [24], and CNN-based
EfcientNetB4 [25].
TABLE 12.2
Comparison of Accuracy of Existing
Methods
Existing Method Accuracy
RVi T 96.6%
ExpDHO-based ShCNN 91%
CNN-based EfcientNetB4 97%
CNN 97.52%
Grad-CAM 98.72%
Proposed ACNN-BTD 99.82%
172 Computational Intelligence Algorithms
12.5 CONCLUSION
Identifying brain tumors is the most challenging task for healthcare professionals
and doctors. Hospital medical professionals require images of the tumors’ appearance and location to diagnose and treat brain tumors. Automatic brain tumor segmentation is a popular method for extracting this data from MRI scans. To resolve
MRI scans of the brain and identify tumor cells, this research presents an architecture called ACNN-BTD. Using a bilateral lter to reduce noise and shrink the image
is the rst step in the preprocessing stage. When applied to the training dataset,
data segmentation involves normalizing the obtained data and performing operations such as translation, rotation, and scaling. ACNN is used to identify the visual
features. All input photos are fed through a network of fully connected ACNNs,
with the training and testing images sourced from the Kaggle dataset. According
to the results of the experiments, ACNN-BTD outperforms all other approaches in
accuracy.
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