Cancerous Tumor Segmentation of Kidney Images and Prediction of Tumor Using Medical Image Segmentation and Deep Learning Techniques

Clinics of Oncology
Research Article ISSN: 2640-1037 Volume 4
Cancerous Tumor Segmentation of Kidney Images and Prediction of Tumor Using
Medical Image Segmentation and Deep Learning Techniques
Akram Z*
, Kareem MS, Mughal B, Ahmed Z, and Aziz S
Department of Computer Science and IT, Faculty of Science, Khwaja Fareed University of Engineering and IT, Rahim Yar Khan,
Pakistan
*
Corresponding author:
Zaib Akram,
Department of Computer Science and IT,
Faculty of Science, Khwaja Fareed University of
Engineering and IT, Rahim Yar Khan, Pakistan,
Email: akram.zaib155@gmail.com
Received: 11 Feb 2021
Accepted: 05 Mar 2021
Published: 09 Mar 2021
Copyright:
©2021 Akram Z, et al. This is an open access article distrib-
uted under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and
build upon your work non-commercially.
Citation:
Akram Z, Cancerous Tumor Segmentation of Kidney
Images and Prediction of Tumor Using Medical Image
Segmentation and Deep Learning Techniques. Clin Onco.
2021; 4(2): 1-9.
Keywords:
Tumor Detection Cancer; Kidney Images;
Deep Learning; Modified CNNs
clinicsofoncology.com 1
1. Abstract
Tumor Detection or Kidney Carcinoma Tumor is type of Kidney
Cancer, which is very hazardous for health. There is a need of a
proper analysis on this disease to treat the patients Kidney Tumor.
Therefore, we have proposed an intelligent Tumor detection sys-
tem. The main purpose of this research is to develop and imple-
ment an automated method that will help to detect and classify the
Kidney Cancerous Tumor by processing Kidney images of Kits
19 dataset. We have used Kidney Tumor Segmentation dataset for
testing our proposed methodology, it contains 300 CT-Scans im-
ages of Kidney Cancer patients. We have applied preprocessing
techniques on these images such as morphological filtering, to en-
hance images. After obtaining the resultant image we have applied
augmentation. We have used modified form of Convolutional Neu-
ral Network for classification purpose. The proposed technique
provides a sophisticated diagnosis and 3 cross folds of prediction
and validation loss with dice score and cross-entropy when com-
pared with previous techniques. The parameter we used to validate
the performance of our proposed technique is cross foldings, dice
score and cross-entropy.
2. Introduction
The chief clinical application of kidney imaging comprises ex-
cluding rescindable reasons of acute kidney grievance, like uri-
nary obstruction, or recognizing permanent Chronic Kidney Dis-
ease (Kidney Carcinoma) that prevents unnecessary diagnosis like
kidney biopsy [1]. Its noninvasiveness, little cost, lack of ionizing
radioactivity, and wide accessibility make it a gorgeous option for
recurrent monitoring and complement of the longitudinal variation
in kidney dimension and sonographic features of kidney cortex
related to kidney useful change. Though, the high individual vari-
ability in image gaining and clarification makes it problematic to
interpret experience-based calculation into consistent practice, like
invasive blood serum creatinine amount. Up till now, noninvasive
imaging methods for organ practical and structural classification
have been progressively investigated directing to diminish the in-
vasive method in both problem-solving and screening situations.
[2] has newly anticipated an inimitable pediatric Kidney Carci-
noma maintenance model corresponding cost and minimizing
incisiveness with the development of risk estimation by utilizing
kidney CT imaging to predict the advance of Kidney Carcinoma
and clinical conclusions in infants.
Conservatively, nephrologists lean towards kidney dimension,
volume, cortical width and echogenicity to assess the sternness
of kidney injury. Identical short renal measurement (e.g., <8cm),
deceptive white cortex, limited capsule contour, entirely indicate
a permanent kidney failing procedure with great specificity but
restricted sensitivity [3]. Additionally, whether these scans param-
eters can be utilized to predict precise Estimated Glomerular Fil-
tration Rates (eGFRs) remains argumentative. For example, [3][4]
studies have described that even though kidney dimension is highly

Volume 4 Issue 2 -2021 Research Article
detailed in detecting permanent Kidney Carcinoma, its association
with eGFR was solitary week to modest, ranging no connotation
to 0.66. Even, researches using the conventional modification diet
in renal disease research (MDRD) equation to evaluation eGFR
(MDRD-eGFR) are measured, the finest correlation well-known
between kidney dimension and eGFR has been 0.36. Likewise,
a fair-to-moderate association of kidney dimensions and cortical
echogenicity through eGFR has been stated. By disparity, cortical
thickness looks to be healthier connected with MDRD-eGFR than
the length of kidney, with a association coefficient as top as 0.85
[5]. Nevertheless, the research writing the correlation coefficient
with 0.85 was attained for a minor sample of 42 grown people with
Kidney Carcinoma without justification.
Yapark et al. established a Kidney Carcinoma scoring scheme in-
tegrating 3 CTScanss parameters, specifically kidney dimension,
parenchymal width, and echogenicity, to recover the correlation;
though, the association was modest (r=0.587). [6] Also, the allot-
ted score of each parameter remainders subjective with indetermi-
nate inter-observer consistency.
To get rid of substantial inter-observer inconsistency in kidney
CTScans interpretation, Machine Learning (ML) provides a com-
pact and objective basis for analytic calibration to notify clinical
decisions. Current advances in imaging segmentation, registration,
and classification through Deep Learning (DL) have significantly
expanded the opportunity and scale of imaging analysis [7]. DL
adapted to diagnostic applications may diminish unnecessary and
offensive procedures, therefore greatly refining the sustainability
and efficiency of current healthcare schemes.
Furthermore, with the extraordinary increase in computer science
performance and real-time computer assisted diagnosis may addi-
tionally change telemedicine and mobile telecare. In the existing
Kidney Carcinoma care prototypical, it relics controversial kidney
role should be regularly partitioned in all symptom-less adults. The
utmost commonly utilized Kidney Carcinoma screening assess-
ments comprise testing the urine and blood for protein or serum
creatinine; though, there is no decisive evidence signifying which
screening trial is more suitable to the further in the situation of
routine screening. Evolving an easily obtainable and non-invasive
image indicator of kidney role using deep learning methods there-
fore may deliver a valuable courtesy tool for identifying Kidney
Carcinoma. To discover this opportunity in clinical preparation,
we established a deep learning algorithm which is based on kidney
CTScanss imaging and medical data in a bulk registry-based Kid-
ney Carcinoma cohort Figure 1.
2.1. Deep Learning in Medical Imaging
In healthcare, particularly in haematology, which is essentially
cancer-related. One of the cancer challenges is that a lot of cancers
are curable and diagnosed early on today. In order to detect wheth-
er there is cancer or not, an image was taken of a patient. You need
a doctor in order to diagnose cancer. As the number of experts is
also reduced. So, if we use deep learning, artificial intelligence or
automation here, then the machine will decide if a specific patient
has cancer or not with a certain amount of precision. The detection
process could be quicker than the specialist [11, 12] to manipulate
the prediction or detection process of a disease such as cancer.
Figure 1: Body Structure of Kidney
2.2. Motivation for selecting Deep Learning
In more than 3000 Kidney diseases affect 2 in every 3 people. It is
very difficult to diagnose. 50 percent of the diagnoses in a public
hospital is incorrect. The Kidney problem is extremely costly be-
cause of unnecessary visits, tests and treatments to general practi-
tioners because of incorrect diagnoses. Most people have also no
access to a Hematologists. So, Machine Learning and Artificial In-
telligence can be used to support image classification using Deep
Neural Networks (DNN). When an image, data, or information
of Kidney is input to a machine it recognizes the morphology of
the Kidney disease and produces some kind of different diagnosis
as the earlier prediction. To provide decision support this can be
faster and better than the other traditional methods. ML and Deep
Learning improve the knowledge and skills in primary care and
substitutional detection in cost.
As there are two kinds of Kidney Tumors, malignant Tumor De-
tection and non-malignant. Malignant Tumor has a dangerous im-
plication because it can extend and damage other portions of the
body parts. In the case of the deadliest diseases like Tumor, detec-
tion in early stages play a vital role in finding the probability of
getting cured. At the primary stage, it is a challenge for a medical
practitioner to differentiate a common Kidney tumor from modest
benign tumors.
Kidney cancer has impacted many people’s lives and the diagnosis
of kidney cancer plays an important role in assessing the likeli-
hood of survival in the early stages. To promote early detection
and affordably warn people who may be at risk, we can use ad-
vanced Deep Learning techniques. This instrument is not always
precise and is not meant to be a substitute for clinical treatment.
The core contributions of this research are as described below:
a. In order to produce a high quality and balanced image, certain
pre-processing techniques are carried out with test sets built only
from images of tumors.
b. For noise removal, we adapted the morphological filtering to

shrink image regions and Conversion into Grey-scale as it is dif-
ficult to operate an RGB image. We perform a filtering process
named Black Hat, as it converts an image including the objects
of the input image. We applied thresholding on images as it is a
segmentation process. This is done to separate object pixels from
background pixels. After that, we reconstruct our missing part of
images using image Inpainting.
1. To balance the imbalanced data, we have used augmentation
techniques to increase the training dataset.
2. We used Modified CNN model for the classification of our
model.
3. Related Works
The existing techniques used for Kidney Cancer detection and
classification are efficient but need some improvements for the
sake of accuracy. Most of the work done can classify Kidney tu-
mors, however, limited work is done on the classification of mul-
tiple diseases. In recent researches, various techniques have been
introduced by the researchers to diagnose and differentiate Tumor
Detection from other Cancers.
suggested two methods for the neural network, a back- propaga-
tion method, a radial-based purpose and a non- linear Support Vec-
tor Machine classifier. (SVM) and, in agreement with their perfor-
mance and precision, equated. To achieve the best procedure with
the above 3 procedures for Kidney Stone Diagnosis prediction,
they used the method for completion. The main goal of their work
was to provide the best medical diagnostic system, such as kidney
stone identification, to minimize the time of analysis and improve
skills and accuracy.
A data pre-processing, transformation, and mining method was
suggested in [9] to obtain information on the collaboration be-
tween several computed constraints and patient life. The two sep-
arate deep learning processes were used in the form of decision
rules to obtain knowledge. For their medical significance, essen-
tial variables detected by data mining were inferred. In their study
work, they launched a new concept, which was used and analyzed
using data obtained at 4 dialysis sites. In their paper, the suggested
approach proposed to minimize the cost and work of selecting pa-
tients for medical studies.
A new work using machine learning processes was proposed in
[7], Support-vector machine and random- forest, respectively.
These data sets with evolving kernels and kernel parameters were
applied to training, categorizing, and assessing kidney stone dis-
ease. The results of both were compared with different kernels
separately, and the proper boundary set was tuned. To build suc-
cessful learning processes for predictions, the findings were well
examined. It is determined that different outcomes with different
kernel tasks were analyzed with the sport vector machine classifi-
cation method.
Two layers are feed forward perceptron trained with back-prop-
agation training procedure and Radial-based feature systems for
study of kidney stone infection in [10] suggested learning vec-
tor-quantization. The performance of all three neural networks was
focused on their accuracy, algorithm development time and data
set size training. Finally, the creators determined from the findings
that a multi-layer learning model trained with back propagation is
the best approach for the diagnosis of kidney stones.
A new technique for the diagnosis of optic-nerve disease was
proposed in [6] through the study of electroretinographic form
signs with the help of artificial neural networks (ANN). The per-
formed method of Multilayer back propagation. The final results
were graded as being solid and diseased. The verified results sug-
gested that an efficient study could be provided by the predicted
technique.
The Subsequent Edge Distance and Pixel Wise Grouping Net-
works (SBD-PCN) for spontaneous kidney segmentation image
CTScans were proposed in [11]. First, the authors defined pre-
trained deep neural networks to categorize images from CTScans
images for high- level images, then used these functions as inputs
with a mid-distance regression network to learn kidney limitations,
and finally categorize the standard boundary space maps into kid-
ney pixels with a pixel arrangement network. In a final learning
technique, the segmentation of the kidney image is built on deep
CNNs to measure kidney and kidney edges.
The Adaptive Neuro-fuzzy Inference Method (ANFIS) suggested
in [12] the prediction of chronic renal failure. Here, the number of
fuzzy rules refers to the number of relationship functions of input
variables. ANFIS neural networks were used to classify glomeru-
lar categorization rates and to correlate system accuracy. To guess
very accurate GFR, modeling and prediction results from ANFIS
networks display the models based on the fuzzy system. Signif-
icant alerts from physicians are the efficacy and frequent moni-
toring of RCC (Kidney Carcinoma) patients in minimizing dis-
ease development and preventing such difficulties. This research
for final help in the field of kidney disease and kidney carcinoma
management in a numerical way was a key method for raising its
problem in the use of the successful prediction model.
The Pattern Discovery Algorithm based on the mentioned revised
joint material (PDA-ADMI) in chronic kidney failure was suggest-
ed in [13]. A reviewed version of popular knowledge is given in
this paper to distinguish the negative and positive relationships.
The writers revealed 16 real patterns that were physically exam-
ined by a medical doctor about the disorder. They proved that by
popular details and incorporated n-class recommendation doc-
trines into the PDA-ADMI model, the group is super additive to
decrease the calculation complexity. Using data conditions, algo-
rithm analysis was carried out and medically assembled to consist
of 16 patterns.

The Pathological Approximate Fibrosis Score (PEFS) on deep
learning for the identification of chronic renal syndrome was sug-
gested in [14]. A deep learning approach was applied to combine
patient-specific histological entities with scientific phenotypes,
such as serum creatinine, biopsy nephrotic proteinuria, kidney car-
cinoma, 1 to 3 and 5 years’ renal survival. The suggested CNN
models surpassed PEFS by classification jobs and predict the point
for the Kidney Carcinoma more precisely than the system PEFS.
[15] proposed an assessment of chronic kidney diseases by the Sup-
port Vector Machine and Artificial Neural Network (SVM- ANN).
All misplaced data values have been substituted for experimenta-
tion by means of the corresponding characteristics. Subsequently,
SVM aims to provide the optimum parameters and ANN relies on
numerical analysis. In addition, ANN and SVM were recognized
by the Enhanced parameters.
Their research arrangements with the ’HMANN’ methodology,
which has been suggested for the identification of kidney stone
procedure, to get rid of various problems as argued in the over-
head survey. In addition, the highest accuracy is demonstrated by
ANN and wavelets for clinical organizational outcomes. In addi-
tion, if data and time are insufficient, the diagnostic syndrome can
be helpful if incomplete diseases are identified and detected [16].
This paper uses ANN to deliver an excellent technique for a min-
imal diagnosis.
4. Dataset Collection
We have used KiTS19 (Kidney Tumor Segmentation Dataset
2019) openly available on Kaggle, to achieve segmentation and
classification of tumors. This set contains upto 1000 images. We
will be creating high quality,A balanced dataset of test and valida-
tion sets constructed solely from tumor images with one associated
image. This is critical because if our model learns from an image
of a given tumor, our accuracy would be unrealistically exaggerat-
ed if it is checked on another image of the same tumor. A sample
image taken from the dataset is shown in Figure 2.
Figure 2: Sample Image taken from Dataset
5. Proposed Methodology
This section explains the working flow of the proposed method
using Deep Learning techniques.
5.1. Overview
On the (KITS19) dataset, we want to perform a 3- fold cross-vali-
dation for kidney tumor segmentation. To accomplish this, we are
using our medical image segmentation techniques with with Con-
volutional Neural Networks and Deep Learning.
The goal of this study is to provide a quick construction of pipe-
lines for medical image segmentation, including data input/output,
preprocessing, data augmentation, patch-wise analysis, metrics, a
library of state-of-the- art deep learning models and model utili-
zation such as training, prediction and fully automated evaluation
(e.g. cross-validation). With Tensor flow as the backend, our ap-
proach is based on Keras.
We have selected 300 kidney cancer patients CT scans images
from dataset. One of three groups had to be named for each pixel:
history, kidney or tumor. The image resolution of the initial scans
is 512x512 and an average of 216 slices (highest slice number is
1059). A block diagram of our purposed methodology is shown in
Figure 3.
Figure 3: Proposed Methodology
5.2. Pre-processing of Dataset
Kidney images are mostly exposed to noise mainly due to bad
light, noise and air bubbles. It includes noise removal, resizing of
image and shading removal. These steps provide a way for fur-
ther steps. We find useful information from the pre-processing of
metadata. Exploratory data analysis is also lying in this section.
Medical images are most prone to noise such as Gaussian noise.

In general, preprocessing techniques can handle the noise of the
images and deep learning techniques learn to avoid these noises
while making classification and predictions, though their ability to
learn is conditioned upon the availability of large training data. In
the case of medical Kidney images datasets, which are mostly im-
balanced datasets, the Deep Learning models are easily susceptible
to overfitting, i.e. they utilize visual hints such as noise indicators
of the cancer category.
The first step in the pipeline is to establish a Data I/O. It offers
the utilization of custom Data I/O interfaces for fast integration of
your specific data structure into the pipeline.
We have used data preprocessing techniques to remove noise and
noises on the images. We have modified the noise removal algo-
rithm. The steps include:
5.2.1. Conversion into Gray-scale: The arrangement of sharp-
ness, shadow, contrasts and construction of the color picture are
novel procedures. To alter the Red Green Blue (RGB) into gray-
scale, an average method is used.
5.2.2. Conversion into the Black-Hat: The black hat is an im-
age filtering process. We have used this filtering for the following
reason. It proceeds an image that contains the basics of the input
image that are lesser than the configuring component and dimmer
than their surroundings.
5.2.3. Morphological Filtering: We have used the structuring ele-
ment as a morphological operator. It affects the regions of images.
We apply this filtering for eliminating the unnecessary regions in
the images on the pixels of the image. Following is the basic flow
of morphological filtering shown in Figure 4.
The crosses shown in Figure. Similar to the origin of the structur-
ing feature, 4. It is very important to locate this place of origin.
When we pass it over the image pixels, it sets the window’s refer-
ence location, and thus the structuring element.
Figure 4: Morphological Operations
5.2.4. Binary Thresholding: It is a modest procedure of image
segmentation. It is a technique to produce a binary image from an
RGB image or grayscale. Typically, we use this to separate fore-
ground pixels from the background pixels.
5.2.5. Inpaint Telea: In Inpaint methodology, we split the image
into two components: the images of the structure and texture, and
then separately restore the two components according to their im-
age characteristics. Using the exemplar-based process, the texture
image is repaired, since the method is derived from the techniques
of texture synthesis. Fig. Figure 5 As for the structure image, the
Laplacian operator is applied first to boost the structure knowl-
edge and to obtain the Laplacian image. To repair the Laplacian
image, the exemplar-based method is then used. Afterwards, via
the Poisson equation, we reconstruct the structure image from the
restored Laplacian image and thus obtain the repaired outcome of
the structure image. Finally, to achieve the painting output of the
original image, these two sub-images are added back together. By
this means, both structure and texture information is well restored.
Figure 5: Framework for Inpainting technique
5.3. Pre-processed Data
As to validate our model properly, we have preprocessing steps.
We create high quality, the balanced dataset for testing and val-
idation as our downloaded dataset was unbalanced. We used to
remove noise in pre-processing that was mainly due to bad light,
noise and air bubbles. These steps (conversion into grey-scale,
morphological filtering, conversion into a Black hat, applies
threshold, Inpaint) provide us clear enhancement to further steps.
The preprocessed results are shown in Figure 6.
Figure 6: Preprocessed Data
5.4. Augmentation
To improve the learning capability of the model and to generalize
its performance, image data augmentation is used. Augmentation
is a technique that is used by making updated copies of images
in the dataset to artificially increase the size of a training dataset.
After the Data I/O Interface initialization, the Data Augmentation
class can be configured.
A Preprocessor object with default parameters automatically ini-
tialize a Data Augmentation class with default values, but here we
initialize it by hand to illustrate the exact workflow of the meth-

odology.
The parameters for the Data Augmentation configure which aug-
mentation techniques should be applied to the data set. In this case,
we are using all possible augmentation techniques in order to run
extensive data augmentation and avoid overfitting.
5.4.1. Selective Data Augmentation: Noticing that our dataset
is still imbalanced, for our training set, we selectively augmented
each class. For each image in a given class, we used Keras Image
Data Generator to generate several augmented images. We pro-
duced more augmented images for the classes with less images to
end up with a training set where all classes are relatively portrayed
equally.
5.4.2. Augmentation Multiplier: If there were 4000 images in
our largest class and 200 images in our smallest class, our aug-
mentation multiplier for the smallest class will be 20. We will thus
produce 20 augmented images for each image in the smallest class.
The largest and smallest classes would then be balanced if we left
our largest class alone. For our real dataset, below is an imple-
mentation of this theory. First, for every class name and its related
index, we construct a data frame and then we describe our ’aug-
multiplier’ in terms of the size of our largest class.
5.4.3. Generate Augmented Data: Note that at this stage, we are
not normalizing our data because when we load up our data for
model training, we will normalize based on Image-Net stats. It
was possible to change the hyper parameters for this Image Data
Generator, we selected these parameters to generate ample variety
while preserving the overall structure. For now, our dataset con-
tains only the augmented images shown in figure 7 that we have
just created. With these augmented images, we will then add the
original images.
5.4.4. Append Original Images: When we use the Keras Image
Figure 7: Modified CNN Architecture
Data Generator, several slightly changed versions of our original
images are loaded. Our training archive, therefore, contains none
of our original photos. We will now append the original photos
into the training folder to complete our dataset. In addition, we will
add the photos that have been set aside to their relevant folders for
testing and validation. Our complete dataset after augmentation is
shown in Figure 11.
Figure 11: Ground Truth and Prediction of Case 44 with Slice 0
5.4.5. Augmented Data: After the process of artificial expanding
of the data set, an augmented dataset is received that dataset ready
to use for learning in our model. This data improves our model
performance as mention above in the augmentation section.
5.5. Model Building
5.5.1. Classify Kidney Images: In our build model section, we
have introduced Fastai methodologies. In the Fastai method, the
first step is to initiate the CNN Model. Then we train the model on
our training data and find the optimal learning rate.
5.5.2. Introduce Fastai Methodologies: Since theAPI is designed
on top of the lightweight PyTorch, we decided to use fastai and
natively supports some nice approaches that are not easily imple-
mented in other frameworks. Below, some of the basic advantages
we get from these strategies are covered and a description of why
they benefit. The modified Model of CNN is shown in Figure 7.
5.5.3. Wd: As an alternative to L2 regularization, this weight de-
cay concept was added. As with other techniques of regularization,
this concept is used to decrease network weights over time, elim-
inating over-fitting.
5.5.4. Lr. Find: As the most important hyper-parameter for train-
ing a neural network, the learning rate is generally accepted. This
technique goes through a trial run, beginning with a low learn-
ing rate and growing it with each batch exponentially. From the
loss vs. learning rate map, we can choose the highest learning rate
where our loss is still falling dramatically.
5.5.5. Fit-One-Cycle: In our approach to setting the learning pace,
this extremely effective technique emerged in [47] and is another
leap forward. In short, this approach works by sweeping through
a variety of learning rates and helps the model to converge faster
and find better local minima (by not getting stuck in the first valley
it comes to).
5.5.6. Max-warp: This is a hyper-parameter of image transfor-
mation provided in a few augmentation libraries. Warp simulates
tilting the view that the image was taken from. In our case, this
is particularly true because most of the images in our dataset are
taken from directly above the tumor of the kidney. Our model may
not be prepared to identify the tumor if we checked this model on
a new picture and took it from a more oblique angle. Our model
becomes more stable for new data by applying warp to our dataset.
5.6. Instantiate CNN model
CNN is low-power, low-latency and small models parameterized
to fulfill the resource limits of a type of use case. Figure 8 shows
the CNN architecture. CNN can be used for recognition, segmen-
tation, embedding’s and classification similar to how other famous
models, like Inception, are used. CNN recovers the state-of-the-
art performance of mobile models on numerous tasks and targets
as well as across a spectrum of dissimilar model sizes. CNN can
be run efficiently on mobile devices with TensorFlow Lite. The

processing of this section is shown in Figure 8.
Figure 8: Processing of CNN
5.7. Training Model for Classification of Tumor
We have to train our model to find the optimal learning rate. Set
our learning rate to the value where learning is fastest, and loss is
still decreasing. We developed a function that uses our input as
an anchor and sweeps through a range to search out the best local
minima. The model validation loss and cross entropy with dice
scores of three folds are shown in (Figure 14-22). We can see that
losses decrease exponentially as the validation of the number.
Figure 14: Validation Score of our model CV-1
Figure 15: Validation Loss of Our Model CV-1
Figure 16: Validation Cross-entropy of Our Model CV-1

6. Experimental Evaluation
6.1. Tumor Segmentation
2D image segmentation, results for each case were analyzed in
three dimensions, with specific criteria for inclusion and exclusion
in the final volumetric segmentation. Kidney regions with tumors
of different types were validated depending upon conditions of
volume and position. The ground truth and prediction of our build
model is shown in Figure 9.
Figure 9: Segmentation and Detection of Tumor after 3 folds of Cross
Validation
Prediction of the ground truth of different cases with slice 0 is
shown in (Figure 10, 11).
Let’s take a look at some of the samples that were most difficult for
our model to detect. Figure 9 shows some of the random samples
that were difficult for our model to detect. At every sub-image and
some parameters are shown. These parameters are: • Prediction:
tumor predicted by the model • Actual: Actual tumor that the mod-
el could not predict. • Loss: Losses occur during predicting the
random image. • Probability: Probability of the model to predict
the tumor correctly.
Figure 10: Ground Truth and Prediction of Case 24 with Slice 0.
x
The model predicted it as Malignant Tumor Detection, however it
was Benign. The losses occur during the prediction of this random
image 0.01 and the probability to predict this random sample was
0.
6.2. Performance of Model
We provided some random images one by one to the model. Since
our main focus was to detect Kidney tumors. The ‘prediction inter-
val’ guesses in which value a future individual reflection will fall,
whereas a ‘confidence interval’ displays the likely value of ranges
related to the statistical constraint of the information, i.e., the mean
of the population.
Our model does not only detect Tumor Detection, but it can also
detect other Kidney tumors with an impressive less loss. Figures
below shows the performance of our model.
In the figures above it can easily be seen that Validation Loss in all
of the folds is decreasing eventually with respect to time.
7. Conclusion
In this research work, we had an image of a CT- Scan of a kid-
ney containing a tumor. We applied the Morphological Filtering
for the Image Enhancement followed by Histogram Equalization.
The restored image went under Image Segmentation, namely, Wa-
tersheds after which Marking was done and the final image was
produced. The final image showed a distinct location of the stone
in the kidney. Hence the stone was detected. We used the CNN

model for testing our sets. We train the model on our training data
and found the learning rate of the model. Then we estimate the
confusion matrix and accuracy of the trained model. Accuracy of
our trained model range from 90-99 percent and are considered
as the best accuracies. The model predicted the Tumor Detection
with confidence level ranges from 70-100 percent depending on
the resolution of the image. The model also successfully predicted
and classify other Kidney tumors with a very high confidence. For
future work, one can suggest the implementation of this automated
method for the detection of Kidney tumors into smartphones/Tabs.
We can also optimize the learning rate of the classifiers.
Kidney images can be sliced into layers so that more accurate de-
tection may perform at higher accuracy.
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