Kidney Cancer Detection
Asif Nazeer
Afifa Marrium
AUIC-21SG-BSSE-5351
AUIC-21SG-BSSE-5466
Supervisor: Ms Faiza Habib
Introduction
Kidney cancer develops when cells in your kidneys
change and grow out of control. People with kidney
cancer may notice flank pain, high blood pressure,
and other symptoms. Kidney cancer is most
common in people between the ages of 65 and
74[1]. Men are twice as likely as women to develop
the disease. Kidney cancer is a major health
concern, with early detection crucial for improving
patient progress. Traditional diagnostic methods like
CT scans and blood and urine tests, can be
subjective, time-consuming, and prone to errors.
Computer science offers the potential to develop a
computer-aided diagnosis (CAD) system for more
accurate and efficient kidney cancer detection.
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Ref Title
Methods/ Techniques
Weakness
Strength
[2]
Machine LearningBased Chronic Kidney
Cancer Prediction
Application
SVM, Decision Tree, Random Forest, KNN,
Native Bayes, Logistic Regression, LGBM
The predictive model
developed in this study
may be specific for
dataset used for training
and may not generalize
well to other healthcare
settings without further
validation
The application may provide real-time
insights into individual risk factors for
chronic kidney cancer, allowing for
timely interventions and personalized
healthcare strategies.
[3]
Deep learning for endto-end kidney cancer
diagnosis on multiphase abdominal
computed tomography
end-to-end deep learning model,
convolutional neural networks (CNNs)
The effectiveness of deep
learning algorithms relies
heavily on the quality and
quantity of the training
data. Limited or biased
datasets could affect the
performance and
generalizability of the
developed models.
Once developed and validated, deep
learning models for kidney cancer
diagnosis can be scaled up and integrated
into clinical practice, potentially benefiting
a large number of patients by providing
faster and more accurate diagnoses
[4]
Kidney tumor
segmentation from
computed tomography
images using
DeepLabv3+ 2.5D
model
the DeepLabv3+ 2.5D model with DPN131 encoder and a post-processing
technique were used.
Deep learning-based
medical devices, including
segmentation software,
may require regulatory
approval from health
authorities Obtaining
regulatory clearance can
be a complex and timeconsuming process that
adds additional hurdles to
the adoption of the
technology in clinical
settings
Deep learning models can be trained on
large datasets to adapt to variations in
tumor morphology and imaging
characteristics, potentially improving
generalization across different patient
populations and imaging protocols.
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Problem Statement
Cancer cells are continuously growing
in the kidney. Being a computer
scientist, the major issue is to analyze
and detect the number of cells and the
required prediction value of cells.
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Methodology
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•
Data Collection
The initial stage is to collect a large dataset of kidney cancer
cells and healthy kidney cells for training the ML algorithm.
• Pre-processing
preprocess the collected data to eliminate errors, missing values,
and irrelevant features. This includes standardizing the data format, dealing with outliers,
and normalizing numerical values to maintain dataset uniformity. Identifying the most
relevant features or attributes that lead to the detection of kidney cancer cells requires
the use of feature selection approaches such as correlation analysis, mutual information,
or tree-based methods like random forests.
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Classification:
Machine learning classification for kidney cancer detection entails training models to
discover patterns and features in data that can distinguish between healthy and malignant kidney
instances. Several machine learning models are utilized for this purpose including.
•
Decision Trees
This technique is useful for identifying kidney cancer cells since it generates a
flowchart-like structure to make decisions depending on input data.
•
Random Forests
By building several decision trees and combining their results, random forests can
deliver more precise classifications.
•
Support Vector Machines (SVM)
SVM is capable of classifying situations by determining the optimum hyperplane
that separates distinct types of data points.
•
Artificial Neuronal Networks (ANN)
ANN are techniques that process information by imitating the neuronal structure
of the human brain. ANN's capacity to discover patterns and relationships within data makes it useful
for recognizing complicated patterns linked with kidney cancer cells. Furthermore it can process vast
amounts of data efficiently.
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• Prediction:
To predict the volume of cancer cells in percentage, we were using
machine learning algorithm (linear regression, decision trees, random forest, RNNs) that
predicts volume percentage over time based on time series data.
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Dataset
• We were using the Kaggle (kidney cancer dataset) in order to detect kidney cancer.
Link: htts://www.kaggle.com/datasets/atreyamajumdar/kidney-cancer
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Functional and Non-Functional
Requirements
Functional Requirement:
•
Data Collection
The system should be able to collect data on kidney cancer, such as CT scan images.
•
Data Preprocessing
The system must be able to preprocess the information obtained which includes
extraction normalization and extraction of features to ensure data quality and consistency.
•
Feature Selection
The system should be able to detect and choose the most relevant characteristics
from the acquired data such as localizing the growing cancer cells.
•
Model Training
The system should be able to train a variety of machine learning models including
decision trees support vector machines, and neural networks with the given features.
•
Model Evaluation
The system should be able to assess the trained models using acceptable
performance metrics such as precision recall and F1-score to find the optimal model for detecting kidney
cancer cells.
•
Prediction
The system should be able to detect the localize cancer cells and predict the volume of
cancer cells using the most effective machine learning model.
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Non-functional Requirements:
•
Accuracy
The system should generate accurate forecasts that will assist healthcare
practitioners in making informed decisions about kidney cancer detection.
•
Efficiency
The system should be capable of analyzing massive volumes of data and
making speedy predictions to save medical staff time.
•
Scalability
The system should be able to deal with an increasing number of patients and
data, ensuring its long-term usefulness.
•
Reliability
The system's data storage processing and prediction must be dependable for
the results to be trusted.
•
Maintainability
The system should be simple to maintain and update, allowing for the
addition of new data models and features as they become available.
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References
1. American Cancer Society. Key Statistics about Kidney Cancer
(https://www.cancer.org/cancer/kidney-cancer/about/key-statistics.html). Accessed
5/6/2022
2. Uddin, K.M.M., Nahid, M.N.H., Ullah, M.M.H. et al. Machine Learning-Based Chronic
Kidney Cancer Prediction Application: A Predictive Analytics Approach. Biomedical
Materials & Devices (2023). https://doi.org/10.1007/s44174-023-00133-5
3. Uhm, KH., Jung, SW., Choi, M.H. , et al. Deep learning for end-to-end kidney cancer
diagnosis on multi-phase abdominal computed tomography. npj Precis. Onc. 5, 54
(2021)
4. Luana Batista da Cruz, Domingos Alves Dias Júnior, João Otávio Bandeira Diniz,
Aristófanes Corrêa Silva, João Dallyson Sousa de Almeida, Anselmo Cardoso de
Paiva, Marcelo Gattass, Kidney tumor segmentation from computed tomography
images using DeepLabv3+ 2.5D model, Expert Systems with Applications, Volume
192,2022.
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Question & Suggestions
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