Multimodal Deep Machine Learning Presentation template

This is a basic format PPT for Multimodal Machine Learning Take from online resources

1. MULTIMODAL DEEPLEARNING

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