Brain Tumor Detection Using Deep Neural Network.pptx

1. Brain Tumor Detection Using Deep Neural Network
and Machine Learning Algorithm
Authors: Masoumeh Siar, Mohammad Teshnehlab
Published on 2019, 9th International Conference on Computer and Knowledge Engineering (ICCKE)
Cited by 115
Presented by Khalid Abdul Rehman

2. Abstract and Introduction
• Brain tumors can be classified as benign or malignant, and timely detection and treatment are crucial for improved patient
outcomes.
• Deep Neural Networks (DNN), specifically Convolutional Neural Networks (CNN), have been used in this paper to detect brain
tumors using MRI images.
• The use of MRI imaging provides accurate images of the brain and is preferred over other techniques due to its higher contrast
in soft tissue.
• The CNN is capable of automatically and locally extracting features from each image, making it a suitable choice for tumor
detection.
• The CNN achieved an accuracy of 98.67% when using the Softmax Fully Connected layer for image classification. The accuracy
with the Radial Basis Function (RBF) classifier was 97.34%, and with the Decision Tree (DT) classifier, it was 94.24%.
• Sensitivity, Specificity, and Precision were used to evaluate the network's performance in addition to accuracy.

3. Abstract and Introduction
• The Softmax classifier showed the best accuracy among the categorizers used in the CNN.
• The proposed method combines feature extraction techniques with the CNN for tumor detection, leading to an
improved accuracy of 99.12% on the test data..
• A clustering algorithm was used for feature extraction before applying the CNN to improve network accuracy
and reduce medical errors.
• Accurate diagnosis by physicians is enhanced by the proposed method as early diagnosis plays a crucial role in
tumor treatment.
• The proposed method shows promising results and has the potential to contribute to the accurate diagnosis
and treatment of brain tumors.

4. Feature Extraction
• Feature extraction is an important technique in machine learning and image processing.
• It involves creating a subset of features from the initial dataset, which facilitates the learning process.
• Feature extraction helps in reducing the dimensionality of the input data when it is too large.
• The process of extracting a subset of features from the primary feature set is called feature extraction.
• The selected features contain essential information about the input data.
• Feature extraction is commonly used in image processing to identify desired segments or specific shapes
(features) within digital images or video streams.

5. Methodology
Dataset
• The dataset used in the paper consists of brain MRI images from 153 patients.
• It includes images from 80 healthy patients and 73 patients with brain tumors.
• The dataset was divided into train and test sets, with 515 images for training from healthy patients and
1151 images for training from patients with brain tumors.
• For testing, there were 56 images from healthy patients and 170 images from patients with brain tumors.
• The original image size is 512 × 512 pixels.

6. Methodology
Simulation
• Sometimes, fat areas in the images may be mistakenly detected as tumors, or tumors may go unnoticed by
physicians, highlighting the importance of skilled medical diagnosis.
• The paper utilizes a CNN for tumor detection in brain images.
• Additional margins in the collected images were cropped to reduce image noise.
• The combination of feature extraction techniques with the CNN is employed to improve network accuracy.
• The proposed method in the study introduces a new approach by combining a clustering algorithm for feature
extraction with the CNN model.

7. Methodology
Feature extraction method
• The central clustering algorithm is an iterative method used for
clustering.
• It aims to find cluster centers that represent the mean points
within each cluster.
• Data samples are assigned to clusters based on their minimum
distance to the cluster centers.
• In the paper, the first-order clustering algorithm is used for
feature extraction.
• Cluster centers are initially selected randomly, and data points are
assigned to clusters based on similarity.
• This process results in the creation of new clusters.
• Figure 1 in the paper showcases the image obtained after
applying the clustering algorithm to the data.
Fig. 1. Applying the clustering algorithm to the image.

8. Methodology
Convolutional neural method
• Initially, the images were directly applied to the CNN without any feature extraction methods.
• The input image size was 227x227 pixels.
• The architecture used for image identification and classification was AlexNet.
• AlexNet consists of 5 Convolutional layers, 3 Sub-sampling layers, Normalization layers, Fully Connected layers, and a
classification layer.
• The Fully Connected layers in AlexNet have 4096 neurons.
• The classification layer in the CNN distinguishes between two classes: brain tumor patient and normal patient.
• Figure 2 in the paper illustrates the architecture of the utilized CNN.

9. Fig. 2. Proposed CNN to gender detection using MRI images

10. SIMULATION RESULTS AND DISCUSSION
• The CNN achieved an accuracy of 98.67% in accurately categorizing tumor patient and
normal patient images.
• To enhance network performance, a combination of Clustering algorithm for feature
extraction and CNN was employed.
• Various classifiers, including Softmax Fully Connected layer, RBF classifier, and DT
classifier, were used to assess the effectiveness of the proposed technique.
• Accuracy, Sensitivity, Specificity, and Precision were utilized as criteria to evaluate
classifier performance.
• The Softmax classifier in the CNN yielded an accuracy of 98.67%, while the RBF
classifier achieved 97.34% accuracy, and the DT classifier achieved 94.24% accuracy.
• By employing the proposed method (combining Clustering algorithm for feature
extraction and CNN+Softmax), the accuracy improved to 99.12% on the test data.
TABLE I
THE RESULTS OBTAINED FROM THE CNN ON
TEST DATA IMAGES WITH THE
CLASSIFIER.

11. SIMULATION RESULTS AND DISCUSSION
• The proposed method, which combines the feature extraction
algorithm and CNN-SoftMax, was applied to the dataset.
• The results of the CNN using the proposed method are presented
in Table II.
• The accuracy of the proposed method increased to 99.12% on the
test data.
• This accuracy improvement demonstrates the effectiveness of the
combined approach compared to the traditional CNN. TABLE II
THE RESULTS OF THE CNN AND PROPOSED
METHOD ON THE DATA TEST
IMAGES.

12. SIMULATION RESULTS AND DISCUSSION
• Out of the total of 226 test data images, three were misdiagnosed
by the traditional CNN.
• One misclassified image from the traditional CNN was correctly
classified after using the proposed method.
• The images categorized by the proposed network may still contain
some mistakes, as shown in Figure 4.
Fig. 3. Images by the CNN are mistakenly classified.
Fig. 4. Images that are wrongly categorized by
the proposed method.

13. SIMULATION RESULTS AND DISCUSSION
Fig. 5. Network accuracy process. Fig. 6. Network loss process.

14. Conclusion
• The paper presents a new method that combines feature extraction and CNN for tumor detection from
brain images.
• The CNN achieved an accuracy of 98.67% in categorizing images into normal and patient classes using
the Softmax classifier.
• By combining the feature extraction algorithm with the CNN, the proposed method achieved an accuracy
of 99.12% on the test data, outperforming the traditional CNN.
• The high accuracy of the proposed method can assist physicians in accurately diagnosing tumors and
treating patients.
• The study demonstrates the effectiveness of CNNs in automatically detecting tumors and selecting
relevant features in medical images.

15. Thank You