Brain Tumor Detection NeuroLensML PPT latest.pptx

The detection of brain tumors is one of the most critical challenges in modern medical science, due to the complexity of the human brain and the devastating impact that these tumors can have on a patient's health. Brain tumors, which include both benign and malignant growths, can lead to a wide range of neurological symptoms and, in severe cases, can be life-threatening. The early detection and accurate diagnosis of brain tumors are essential for improving patient outcomes, but traditional diagnostic methods, primarily based on medical imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, often face limitations. These limitations stem from the reliance on human expertise, which can introduce subjectivity and variability into the diagnostic process. This is where machine learning (ML), a subfield of artificial intelligence (AI), comes into play, offering a powerful tool to enhance the accuracy, efficiency, and consistency of brain tumor detection.

Machine learning involves the development of algorithms that can learn from data and make predictions or decisions without being explicitly programmed for each specific task. In the context of brain tumor detection, ML algorithms can be trained on large datasets of medical images to recognize patterns associated with different types of brain tumors. These patterns might be too subtle or complex for the human eye to detect consistently. The application of ML in brain tumor detection typically involves several key steps: data acquisition, data preprocessing, model selection, training and validation, and finally, deployment in a clinical setting. Each of these steps is crucial in building a robust system that can assist radiologists and other medical professionals in diagnosing brain tumors more accurately.

Data acquisition is the first step in this process, involving the collection of large datasets of medical images from various sources. These datasets are often annotated by experts to indicate the presence and type of brain tumors, providing the necessary labels for supervised learning models. Publicly available datasets, such as the Brain Tumor Segmentation (BRATS) dataset, have been instrumental in advancing research in this field. However, data acquisition is not without its challenges. Medical images can vary significantly in quality due to differences in imaging equipment, protocols, and patient conditions. Furthermore, acquiring a sufficiently large and diverse dataset is crucial for training ML models that can generalize well to new, unseen data.

Once the data is collected, it must be preprocessed to ensure that it is suitable for use in machine learning models. Preprocessing steps often include normalization, which adjusts the pixel intensity values of the images to a common scale; data augmentation, which artificially increases the size of the dataset by applying transformations such as rotation, flipping, or scaling to the images; and noise reduction.

1. NeuroLensML : Enhancing Brain Tumor Diagnosis With Machine Learning Paper ID: CS9099 P. Jhansi Lakshmi N. Uttej Kumar V. Nanda Kishore-201FA04084, M. Shrilekha-201FA04108, A. Lakshmana Sagar-211LA04001.

2. CONTENT LAYOUT Abstract Existing System Literature Survey Study on existing models Proposed System Dataset Predicted Accuracy Architecture Results Conclusion References

3. ABSTRACT Our application seamlessly integrates ResNet and Sequential models, synergizing their strengths to construct a powerful classification system for MRI image analysis, specifically targeting brain tumor detection. Leveraging MRI technology, our application offers accurate tumor presence analysis, providing precise classification of tumors for informed medical decision-making. Real-world Android Application.

4. EXISTING SYSTEM Due to the complexity of interpretation, the diagnosis of brain tumors often requires a consultation with a specialized neurologist. Web applications alone may not be sufficient for accurate diagnosis because they lack the knowledge and nuanced analysis provided by medical professionals. Appointments with a neurologist can be time-consuming, which underscores the need for efficient diagnostic pathways in health care.

5. LITERATURE SURVEY Paper Method Data Accuracy Strengths Weaknesses Future Work 1 Watershed Segmentation 14 MRI 10% error Offers promise, fast Limited tissue differentiation, 10% error rate Kiosk for detection, user-friendly interface 2 SVM Classification GLCM features 36 tumor , 26 normal MRI 93.05% Surpasses conventional models, high accuracy Manual parameter tuning, limited differentiation, black box nature More data, deep learning, clinical integration 3 CNN using DeepMedic - - Emphasizes precise tumor removal Technical limitations, clinical challenges, ethical considerations Faster, more accurate methods, complex tumor geometries

6. LITERATURE SURVEY Paper Method Data Accuracy Strengths Weaknesses Future Work 4 CNN (Early/Late Fusion) BRATS2017 (210 HGG, 75 LGG) 97.2% (Late Fusion) Adaptable to varying data, high accuracy Computational cost, generalizability, clinical integration Improve late fusion, interpretability 5 Preprocessing, Expectation-Maximization, Ranklet Transform, Autoencoder-SVM BRATS 2012 (2520 malignant, 1150 normal) 94.6% High accuracy, improved sensitivity/specificity Only T2/FLAIR-weighted scans, potential bias Other modalities, bias mitigation 6 CNN, Autoencoder, K-means 253 brain MRI (155 tumorous, 98 normal) 95.55% Automated detection/segmentation, fast Data bias, generalizability, interpretability Enhanced accuracy, clinical integration

7. LITERATURE SURVEY Paper Method Data Accuracy Strengths Weaknesses Future Work 7 Patch-wise CNN BraTS - Optimizes segmentation, improves accuracy Imbalanced data, generalizability Apply to other data, different MRI datasets 8 Histogram equalization, Wavelet decomposition, PCA, Fuzzy C-means - High accuracy, good PSNR - - 9 16 Layer Deep CNN - 97.87% High accuracy, combines FCM and CNN Data dependence, computational cost, black box nature -

8. LITERATURE SURVEY Paper Method Data Accuracy Strengths Weaknesses Future Work 10 Reviews ML/DL for brain tumor detection - - Deep learning outperforms traditional ML, high accuracy Data dependence, interpretability, limitations Feature selection, hybrid learning, transfer learning 11 Reviews brain tumor detection and classification - - Early detection crucial, limitations in detection/treatment Specificity/sensitivity, human dependence, resource constraints Research, technology, healthcare equity 12 NeuroXAI (explainable AI for brain scans) - - Improves understanding of AI findings Not perfect, limitations Refinement

9. LITERATURE SURVEY Paper Method Data Accuracy Strengths Weaknesses Future Work 13 DWT, Deep Neural Network - High accuracy Improved accuracy/efficiency, early diagnosis More data, CNN combination 14 PET BRATS 2020 99.74% High accuracy, outperforms state-of-the-art Data hungry, overfitting, black box, slow - 15 Reviews machine learning for brain tumor diagnosis - - Early detection crucial, machine learning assists Faster, more accurate diagnoses, treatment on target Research-practice gap

10. STUDY ON EXISTING MODELS Based on the literature survey we worked on the four main models on three different datasets. They are Sequential Model Residual Neural Network Position emission transmission 16 Layer deep CNN Their results are as follows:

11. METHOD ACCURACY (15 epocs ) Sequential 95 ResNet 97.5 PET 90 16 deep NN 31 Dataset 1: 7000 MRI images

12. METHOD ACCURACY (15 epocs) Sequential 93 ResNet 96 PET 97 16 deep NN 29 Dataset 2: 2870 MRI images

13. METHOD ACCURACY (15 epocs) Sequential 93 ResNet 96 PET 93 16 deep NN 29 Dataset 3: 3264 MRI images

14. PROPOSED SYSTEM Our Android app combines ResNet and Sequential models to elevate brain tumor detection, ensuring heightened accuracy and reliability in diagnosis. By scrutinizing varied datasets of 2870, 3264, and 7000 images, our system fine-tunes detection accuracy, optimizing performance to meet real-world demands effectively. With our trained model seamlessly integrated into mobile devices, we pioneer accessible brain tumor diagnosis, overcoming barriers to specialized care through convenient and timely detection.

15. DATASET Contain four classes namely Glioma, Meninigioma , Pituitary, No Tumor . Sample Images: Glioma Meninigioma Pituitary No Tumor

16. PREDICTED ACCURACY No. of images in Dataset Accuracy Predicted 2870 86 % 3264 88 % 7000 91 %

17. ARCHITECTURE

18. RESULTS

19. CONCLUSION We initially encountered a significant obstacle in our work – overfitting . In our case, this meant the model might perform well on the training data (brain scans used to train it), but struggle to accurately classify new brain scans, hindering its real-world applicability in diagnosing cancer . To overcome this hurdle, we opted to develop a novel model that transcended the limitations of existing approaches. This new model leveraged the strengths of two current models by combining them in a strategic way. The resulting ensemble model aimed to deliver more accurate and robust results for classifying brain cancer .

20. REFERENCES A . Sivaramakrishnan And Dr.M.Karnan “A Novel Based Approach For Extraction Of Brain Tumor In MRI Images Using Soft Computing Techniques,” International Journal Of Advanced Research In Computer And Communication Engineering, Vol. 2, Issue 4, April 2013 Brain Tumor Detection Using Convolutional Neural Network: Proposes a method to extract brain tumors from MRI images using Fuzzy C-Means clustering followed by traditional classifiers and a convolutional neural network. Achieved an accuracy of 97.87%. (ICASERT 2019 ) Brain Tumor Detection and Classification Using Intelligence Techniques: Offers a comprehensive overview of brain tumor detection, including morphology, datasets, augmentation methods, feature extraction, and categorization of ML and DL models. (IEEE Access 2023) Brain tumor detection and classification using machine learning: a comprehensive survey Explainability of deep neural networks for MRI analysis of brain tumors ----springer Classification using deep learning neural networks for brain tumors Heba Mohsen a, *, El- Sayed A. El- Dahshan b,c , El- Sayed M. El- Horbaty d , Abdel- Badeeh M. Salem d MRI-based brain tumour image detection using CNN based deep learning method A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

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