brain tumor presentation.pptxbraintumorpresentationonbraintumor

Brain tumor presentation

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. 3

4. SYSTEM REQUIREMENTS h 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

5. UML DIAGRAM

6. DATA PREPROCESSING

7. EXPLORATORY DATA ANALYSIS 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 7

8. Class : 1 = Tumor 0 = Not Tumor The less the image homogenity the more possibility that the patient have a tumor T he 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

9. 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.

10. To analyze the F1 score history for brain tumor prediction, you would typically divide your dataset into training and validation sets      

11. 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.

12. Unseen images are seen putting into the model and obtained predictions.

13. 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. 14

14. 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.    

15. 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. 16

16. 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 17

17. 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.  18

18. THANK YOU Guided by: P. Prathima Madam Asst. Professor