In this book, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset. this dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). it also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness.
In this project, various methods and functionalities related to machine learning and deep learning are covered. Here is a summary of the process: Data Preprocessing: Loaded and preprocessed the dataset using various techniques such as feature scaling, encoding categorical variables, and splitting the dataset into training and testing sets.; Feature Selection: Implemented feature selection techniques such as SelectKBest, Recursive Feature Elimination, and Principal Component Analysis to select the most relevant features for the model.; Model Training and Evaluation: Trained and evaluated multiple machine learning models such as Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, and Support Vector Machines using cross-validation and hyperparameter tuning. Implemented ensemble methods like Voting Classifier and Stacking Classifier to combine the predictions of multiple models. Calculated evaluation metrics such as accuracy, precision, recall, F1-score, and mean squared error for each model.
Visualized the predictions and confusion matrix for the models using plotting techniques.; Deep Learning Model Building and Training: Built deep learning models using architectures such as MobileNet and ResNet50 for image classification tasks. Compiled and trained the models using appropriate loss functions, optimizers, and metrics. Saved the trained models and their training history for future use.; Visualization and Interaction: Implemented methods to plot the training loss and accuracy curves during model training. Created interactive widgets for displaying prediction results and confusion matrices. Linked the selection of prediction options in combo boxes to trigger the corresponding prediction and visualization functions.; Throughout the process, various libraries and frameworks such as scikit-learn, TensorFlow, and Keras are used to perform the tasks efficiently. The overall goal was to train models, evaluate their performance, visualize the results, and provide an interactive experience for the user to explore different prediction options.
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