1. NEUROINSIGHTS UNRAVELING BRAIN TUMORS
THROUGH DATA ANALYTICS
Presented by:
• Keshoju Christu Jyothi (122146006)
• Kolipaka Srikanth (122146007)
2. A brain tumor is an abnormal mass of tissue
in the brain. It can be benign or cancerous
and may cause symptoms such as
headaches, seizures, or neurological deficits.
Treatment options include surgery, radiation
therapy, and chemotherapy, depending on
the type and location of the tumor. Early
detection and treatment are crucial for
better outcomes.
BRAIN TUMOR
3. INTRODUCTION
Our project aims to transform brain tumor diagnosis and
treatment using advanced data analytics, detection
methods, and predictive models. We seek to empower
healthcare professionals with insights for early and accurate
tumor identification. Our scope includes exploring brain
tumor data, developing detection algorithms, and creating
predictive models to revolutionize healthcare.
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4. SYSTEM REQUIREMENTS
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Hardware Requirements:
High performance Computing Servers
Graphics Processing Units
Storage Infrastructure
Memory (RAM)
Networking infrastructure
Workstations for developers and analysts
Software Requirements:
Programming languages
Machine Learning
Data sets
Security software
Image Visualization
6. DATA PREPROCESSING
Our data set contains tumor and non tumor MRI images
obtained from various online sources and modeling is
done using python language.
Performed some data preprocessing steps such as
calculating statistics like mean, variance, skewness,
kurtosis, etc and exploring the dataset.
7. EXPLORATORY DATAANALYSIS
• In Exploratory data analysis of brain
tumor data, we examined the distribution
of cases with and without brain tumors.
The pie chart illustrates the breakdown
• You've visualized certain features of the
dataset to gain insights. The patients
with lower image homogeneity are more
likely to have a brain tumor, and you
visualized the count of tumor vs. non-
tumor cases.
• 44.74% of the patients in our dataset
have brain tumor
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8. Cases with Brain Tumors: This segment
represents the percentage of cases where brain
tumors are present. A larger proportion of cases
falling into this category indicates a higher
prevalence of brain tumors within the dataset
Cases without Brain Tumors: This segment
represents the percentage of cases where brain
tumors are absent. A larger proportion of cases
in this category suggests a lower prevalence
of brain tumors within the dataset
9. Class :
1 = Tumor
0 = Not Tumor
The less the image homogenity the more
possibility that the patient have a tumor
The less the rate of randomness in the
brain image the more likely the patient has
a tumor
Count of Tumour patients are less in
that sample random data and count of non
tumor patients are high compare to tumor
patients
10. IMAGE
VISUALIZATION
• Analyzing brain tumors typically
involves medical imaging such as MRI
or CT scans to identify the location,
size, and characteristics of the tumor.
Predictive analysis may involve
assessing factors like tumor growth
rate, cell type, and patient
demographics to predict outcomes such
as treatment response or prognosis.
Machine learning algorithms can also
be used to analyze imaging data and
predict tumor behavior or patient
outcomes based on various features
extracted from the images.
11. To analyze the F1 score history for brain tumor prediction, you would typically divide
your dataset into training and validation sets
12. PREDICTION
To predict brain tumor presence in medical images, you'd typically
start with a dataset where each image is labeled with ground truth
information indicating whether a tumor is present or not. Then, you
would train a machine learning model such as a convolutional
neural network (CNN) using these labeled images.
13. • Unseen images are seen putting into the model and obtained predictions.
14. IDENTIFYING PATTERNS
• The model learns to identify patterns and
features in the images that are associated
with tumor presence. Once trained you can
use the model to predict tumor presence in
new, unseen images by inputting the images
into the model and obtaining predictions.
• Evaluation of the model's performance
would involve comparing its predictions
with the ground truth labels of the test
dataset. Metrics such as accuracy, precision
recall and F1 score can be calculated to
assess the model's effectiveness in
predicting brain tumor
presence in the images.
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15. LOGISTIC REGRESSION
CONFUSION MATRIX
Predicted Results In our brain tumor
analysis model utilizing logistic regression,
we utilize a confusion matrix to evaluate the
performance of our predictions. The
confusion matrix provides a comprehensive
overview of how well our model is
classifying brain tumor presence.
16. RANDOM FOREST CONFUSION
MATRIX
Random Forest Confusion Matrix: Predicted
Results In our brain tumor analysis model
employing Random Forest classification, we
utilize a confusion matrix to assess the
performance of our predictions. The confusion
matrix provides a detailed breakdown of how
well our model is classifying
brain tumor presence.
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17. ADVANTAGES
• Accurate and efficient brain tumor segmentation using
deep learning techniques
• Potential to assist medical professionals in diagnosing and
treating brain tumors more effectively
• Automation of a labor-intensive and time-consuming task,
leading to increased productivity in healthcare settings.
• Improved patient outcomes through early detection and
precise delineation of tumor boundaries
• Scalability of the model for analyzing large volumes of
medical imaging data
• Contribution to the advancement of artificial intelligence
in medical imaging and healthcare
• Opportunity for further research and development in the
field of neural network-based medical image analysis
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18. CONCLUSION
• The analysis revealed that approximately 44.74% of patients in the dataset had brain tumors,
indicating a balanced representation of both cases. Features like homogeneity and randomness
exhibited correlations with tumor presence, suggesting their potential as predictive factors.
• The UNet architecture demonstrated promise in accurately segmenting tumor regions, with scope for
further optimization to enhance generalization. Additionally, logistic regression and random forest
classifiers were employed to classify tumors based on their characteristics, facilitating diagnosis and
treatment planning. Feature importance analysis underscored metrics like entropy and energy, offering
insights for developing robust segmentation models and identifying biomarkers
• Overall, our project underscores the potential of machine learning, particularly deep learning-based
segmentation models, in aiding clinicians with precise and efficient brain tumor detection from MRI
images.
• Looking ahead, exploring additional data sources, advanced deep learning architectures, and
collaborative efforts with medical professionals hold promise for further enhancing early diagnosis
and treatment planning for brain tumor patients.
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